You searched for subject:(imputation).
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Texas A&M University
91.
Erdogan, Niyazi.
Modeling Successful Inclusive STEM High Schools: An Analysis of Students? College Entry Indicators in Texas.
Degree: 2014, Texas A&M University
URL: http://hdl.handle.net/1969.1/153495 
► This dissertation highlights a conceptual framework for specialized Science, Technology, Engineering, and Mathematics (STEM) schools and the college readiness of Inclusive STEM High School graduates…
(more)
▼ This dissertation highlights a conceptual framework for specialized Science, Technology, Engineering, and Mathematics (STEM) schools and the college readiness of Inclusive STEM High School graduates in comparison to traditional high school graduates. In reviewing the literature, I found the current perception for specialized STEM schools can be described as unique environments including advanced curriculum, expert teachers, and opportunities for internships and immersion. Finding from the studies exploring college and career readiness of students attending these schools revealed students from specialized STEM schools are performing slightly better on high-stake mathematics and science tests in comparison with students in traditional schools. Studies also showed students from specialized STEM schools are more interested in STEM, more willing to attend classes, more likely to pass state tests, and more likely to earn college degrees. After synthesizing the literature, I created a conceptual framework of effective learning environments for specialized STEM schools using an ecology metaphor.
In answering the research questions related to success of students attending either T-STEM or traditional schools, I concluded success on reading, mathematics, science high-stake tests for students does not differ by school type. However, student demographic variables (i.e., gender, ethnicity, socioeconomic status, and special education status) may influence success of students attending T-STEM schools. For example, results revealed statistical significance between male, Hispanic, White, and economically disadvantaged students from T-STEM and traditional schools on reading, mathematics, and science scores.
In answering the research question related to success of T-STEM in comparison with traditional schools, I found no statistical significance in measures of schools? success. However, regardless of school type, female students performed better on reading scores whereas male students performed better on mathematics and science scores. In addition, White and Asian students outperformed all other ethnic groups on performance measures. Also, economically disadvantaged students and students in special education program were outperformed by students not identified as disadvantaged or learning disabled. On school level indicators, regardless of school type, dropout rate negatively associated with students? reading, mathematics, and science scores. In addition, percentage of students taking AP/IB end of course exam had a positive association with reading, mathematics, and science scores. Finally, percentage of students taking SAT/ACT also demonstrated a positive association with reading and mathematics scores, but not science scores. In conclusion, specialized STEM schools can be the solution to the problem of shortages in the STEM workforce; however, there still work remains.
Advisors/Committee Members: Stuessy, Carol L. (advisor), Yalvac, Bugrahan (committee member), Capraro, Mary Margaret (committee member), Willson, Victor (committee member).
Subjects/Keywords: STEM education; Inclusive STEM high schools; College and career readiness; STEM schools; Multilevel multiple imputation
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APA (6th Edition):
Erdogan, N. (2014). Modeling Successful Inclusive STEM High Schools: An Analysis of Students? College Entry Indicators in Texas. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153495
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Erdogan, Niyazi. “Modeling Successful Inclusive STEM High Schools: An Analysis of Students? College Entry Indicators in Texas.” 2014. Thesis, Texas A&M University. Accessed December 11, 2019.
http://hdl.handle.net/1969.1/153495.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Erdogan, Niyazi. “Modeling Successful Inclusive STEM High Schools: An Analysis of Students? College Entry Indicators in Texas.” 2014. Web. 11 Dec 2019.
Vancouver:
Erdogan N. Modeling Successful Inclusive STEM High Schools: An Analysis of Students? College Entry Indicators in Texas. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/1969.1/153495.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Erdogan N. Modeling Successful Inclusive STEM High Schools: An Analysis of Students? College Entry Indicators in Texas. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153495
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Anna University
92.
Suresh Joseph, K.
Genetic algorithm based hybrid imputation model for
software effort estimation; -.
Degree: Information and Communication
Engineering, 2014, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/24544
► One of the most exceptionally important tasks in software development life cycle is to produce precise effort estimations and schedule Developers may use effort estimate…
(more)
▼ One of the most exceptionally important tasks in
software development life cycle is to produce precise effort
estimations and schedule Developers may use effort estimate as
input to project planning, budgeting pricing policies and
investment analyses Missing or Incomplete data are exasperation to
a certain extent everywhere but especially in effort estimation it
is a greater hindrance Over estimation of the software effort can
result in newlinelosing the chance to win a bid and underestimation
can lead to detrimental effect on the quality of software or
monetary loss Accuracy in effort estimation is decisive for
developers and customers For a developer it is required to do
preplanning budgeting risk analysis and productivity assessment and
customers requires estimation for taking place in contract
negotiation process The importance of effort estimation is seen on
different newlinephases of SDLC in the initial phase it provides
some insights on whether to proceed forward or not Rough validation
and progress of a project are done in the intermediate phase At the
completion phase cost estimates helps to assess productivity The
major challenge in Software estimation is mainly concerned about
quality of data High degree of inaccuracy is mainly because of
missing values The common measure usually adopted in missing values
is to ignore the whole project data or its feature newline
newline
References p.157-163.
Advisors/Committee Members: Ravichandran, T.
Subjects/Keywords: Genetic Algorithm; Hybrid imputation model; Information and communication engineering; Software effort estimation
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APA (6th Edition):
Suresh Joseph, K. (2014). Genetic algorithm based hybrid imputation model for
software effort estimation; -. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/24544
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Suresh Joseph, K. “Genetic algorithm based hybrid imputation model for
software effort estimation; -.” 2014. Thesis, Anna University. Accessed December 11, 2019.
http://shodhganga.inflibnet.ac.in/handle/10603/24544.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Suresh Joseph, K. “Genetic algorithm based hybrid imputation model for
software effort estimation; -.” 2014. Web. 11 Dec 2019.
Vancouver:
Suresh Joseph K. Genetic algorithm based hybrid imputation model for
software effort estimation; -. [Internet] [Thesis]. Anna University; 2014. [cited 2019 Dec 11].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/24544.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Suresh Joseph K. Genetic algorithm based hybrid imputation model for
software effort estimation; -. [Thesis]. Anna University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/24544
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Pontifícia Universidade Católica de São Paulo
93.
Heitor Donizete de Oliveira.
Imputação objetiva diante da teoria do delito: o que é, onde se situa e qual sua finalidade?.
Degree: 2006, Pontifícia Universidade Católica de São Paulo
URL: http://www.sapientia.pucsp.br//tde_busca/arquivo.php?codArquivo=2308
► This project intends to supply the readers with the means to think about objective imputation specially in criminal law which is considered by some Brazilian…
(more)
▼ This project intends to supply the readers with the means to think about objective imputation specially in criminal law which is considered by some Brazilian criminologists as part of a crime definition. Considering, at first, some constitutional principles and then, taking into consideration what the crime theory is ( crime theory explains how it can be used by the criminal judge,) and, at last, studying the philosophical evolution of the crime theory throughout the time, it was concluded that objective imputation has became a theory itself after the theory of the crime as a target which was named functional criminal law theory. The objective imputation theory has changed the main point of the crime classical theory while considering the external cause of a crime, mainly when a specialist is theorizing about crime action. We should keep in mind that the material cause theory has always taken in consideration that cause and consequence are part of theory of the crime. Later on it was considered the objective imputation in foreigner studies, mainly in Germany, the country where the idea of imputation started as it is known nowadays. And then objective imputation was analysed by some Brazilians criminologists who consider it as a valid and applicable theory. At last, we got to its definition how it appears in crime definition and why it is useful.
Esta tese destina-se a uma reflexão sobre a imputação objetiva em Direito Penal, que vem sendo admitida nos últimos tempos por alguns penalistas nacionais, como elemento integrante da tipicidade penal. Partindo de alguns princípios constitucionais penais e também do que é a teoria do delito e sua função para o aplicador da lei penal e depois verificando a evolução filosófica da teoria do delito, no decorrer do tempo, constatou-se que a imputação objetiva tomou corpo depois do finalismo penal, com a chamada teoria funcional do Direito Penal, tendo a função de corrigir a relação de causalidade material que sempre foi adotada desde a teoria clássica, em termos de teoria do crime. Sendo que em termos, de causalidade material, quase sempre foi adotada a teoria da equivalência dos antecedentes causais. Após, foi feito um estudo da imputação objetiva na doutrina estrangeira, principalmente na Alemanha, berço da imputação, nos termos em que ela é compreendida na atualidade, sendo que depois foi analisada sob a perspectiva de penalistas brasileiros que vêm adotando-a, para, finalmente, concluir-se o seu conceito, onde está localizada na estrutura do crime e qual é sua utilidade.
Advisors/Committee Members: Sérgio Seiji Shimura.
Subjects/Keywords: Responsabilidade penal; teoria do delito; imputação objetiva; Delito; Direito penal – Brasil; objective imputation; DIREITO PENAL
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APA (6th Edition):
Oliveira, H. D. d. (2006). Imputação objetiva diante da teoria do delito: o que é, onde se situa e qual sua finalidade?. (Thesis). Pontifícia Universidade Católica de São Paulo. Retrieved from http://www.sapientia.pucsp.br//tde_busca/arquivo.php?codArquivo=2308
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Oliveira, Heitor Donizete de. “Imputação objetiva diante da teoria do delito: o que é, onde se situa e qual sua finalidade?.” 2006. Thesis, Pontifícia Universidade Católica de São Paulo. Accessed December 11, 2019.
http://www.sapientia.pucsp.br//tde_busca/arquivo.php?codArquivo=2308.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Oliveira, Heitor Donizete de. “Imputação objetiva diante da teoria do delito: o que é, onde se situa e qual sua finalidade?.” 2006. Web. 11 Dec 2019.
Vancouver:
Oliveira HDd. Imputação objetiva diante da teoria do delito: o que é, onde se situa e qual sua finalidade?. [Internet] [Thesis]. Pontifícia Universidade Católica de São Paulo; 2006. [cited 2019 Dec 11].
Available from: http://www.sapientia.pucsp.br//tde_busca/arquivo.php?codArquivo=2308.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Oliveira HDd. Imputação objetiva diante da teoria do delito: o que é, onde se situa e qual sua finalidade?. [Thesis]. Pontifícia Universidade Católica de São Paulo; 2006. Available from: http://www.sapientia.pucsp.br//tde_busca/arquivo.php?codArquivo=2308
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Kansas
94.
Kim, Min Sung.
Linking with Planned Missing Data: Concurrent Calibration with Multiple Imputation.
Degree: PhD, Psychology & Research in Education, 2015, University of Kansas
URL: http://hdl.handle.net/1808/20985
► The purpose of this paper is to introduce a new Item Response Theory (IRT) concurrent calibration method using multiple imputation and investigate its effectiveness by…
(more)
▼ The purpose of this paper is to introduce a new Item Response Theory (IRT) concurrent calibration method using multiple
imputation and investigate its effectiveness by comparing with other equating methods. The 3-parameter logistic (3PL) model is chosen due to its reliable performance and the 2-parameter logistic (2PL) model is also applied to compare the performance. Six equating methods were compared in simulated data studies under a common-item nonequivalent group design, and ability parameters were randomly drawn from various distributions with different combinations of mean and variance. Additionally, the effect of two anchor test lengths on parameter estimation was compared for all conditions. The main focus was on comparing concurrent calibration methods of marginal maximum likelihood estimation (MMLE) and multiple
imputation (MI). For real data, PISA 2000 reading score was applied to several equating methods. Likewise the previous literatures, MI showed better or similar mean squared error (MSE) than MMLE. In addition, the usefulness of Mean Imputed Score, a byproduct of MI was proposed and compared with Observed Score Equating (OSE) and True Score Equating (TSE) results.
Advisors/Committee Members: Skorupski, William P. (advisor), Kingston, Neal (cmtemember), Frey, Bruce (cmtemember), Peyton, Vicki (cmtemember), Wu, Wei (cmtemember).
Subjects/Keywords: Educational psychology; Educational tests & measurements; Educational evaluation; concurrent calibration; equating; IRT; linking; multiple imputation
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APA ·
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APA (6th Edition):
Kim, M. S. (2015). Linking with Planned Missing Data: Concurrent Calibration with Multiple Imputation. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/20985
Chicago Manual of Style (16th Edition):
Kim, Min Sung. “Linking with Planned Missing Data: Concurrent Calibration with Multiple Imputation.” 2015. Doctoral Dissertation, University of Kansas. Accessed December 11, 2019.
http://hdl.handle.net/1808/20985.
MLA Handbook (7th Edition):
Kim, Min Sung. “Linking with Planned Missing Data: Concurrent Calibration with Multiple Imputation.” 2015. Web. 11 Dec 2019.
Vancouver:
Kim MS. Linking with Planned Missing Data: Concurrent Calibration with Multiple Imputation. [Internet] [Doctoral dissertation]. University of Kansas; 2015. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/1808/20985.
Council of Science Editors:
Kim MS. Linking with Planned Missing Data: Concurrent Calibration with Multiple Imputation. [Doctoral Dissertation]. University of Kansas; 2015. Available from: http://hdl.handle.net/1808/20985
Uppsala University
95.
Bucaro, Orlando Olaya.
Predicting risk of cyberbullying victimization using lasso regression.
Degree: Statistics, 2017, Uppsala University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767
► The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis…
(more)
▼ The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis is to specify a prediction model that can accurately predict the risk of cyberbullying victimization. The data used is from a survey conducted at five secondary schools in Pereira, Colombia. A logistic regression model with random effects is used to predict cyberbullying exposure. Predictors are selected by lasso, tuned by cross-validation. Covariates included in the study includes demographic variables, dietary habit variables, parental mediation variables, school performance variables, physical health variables, mental health variables and health risk variables such as alcohol and drug consumption. Included variables in the final model are demographic variables, mental health variables and parental mediation variables. Variables excluded in the final model includes dietary habit variables, school performance variables, physical health variables and health risk variables. The final model has an overall prediction accuracy of 88%.
Subjects/Keywords: Multiple imputation; Generalized linear mixed models; Variable selection; Probability Theory and Statistics; Sannolikhetsteori och statistik
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APA (6th Edition):
Bucaro, O. O. (2017). Predicting risk of cyberbullying victimization using lasso regression. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bucaro, Orlando Olaya. “Predicting risk of cyberbullying victimization using lasso regression.” 2017. Thesis, Uppsala University. Accessed December 11, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bucaro, Orlando Olaya. “Predicting risk of cyberbullying victimization using lasso regression.” 2017. Web. 11 Dec 2019.
Vancouver:
Bucaro OO. Predicting risk of cyberbullying victimization using lasso regression. [Internet] [Thesis]. Uppsala University; 2017. [cited 2019 Dec 11].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bucaro OO. Predicting risk of cyberbullying victimization using lasso regression. [Thesis]. Uppsala University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Ohio University
96.
Parsons, Michael M.
Planned Missing Data Designs in Communication
Research.
Degree: PhD, Communication Studies (Communication), 2013, Ohio University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1376579676
► Prominent among the many methodological challenges communication research faces are the relative lack of longitudinal research conducted in the discipline and the threats to validity…
(more)
▼ Prominent among the many methodological challenges
communication research faces are the relative lack of longitudinal
research conducted in the discipline and the threats to validity
that arise from the complex instrumentation necessary for inquiry
into human interaction. This dissertation presented planned missing
data designs (PMDs) as solutions to these challenges because PMDs
can make research less burdensome, cheaper, faster, and more valid.
Three studies illustrate the use of PMDs in communication
research.Study one was a controlled-enrollment PMD investigation of
the relationship between students' public speaking anxiety and
communication competence in a semester-long longitudinal study. By
using the controlled-enrollment design, this study had five
measurement waves, but each participant was measured at no more
than three measurement waves. Results indicated that the
controlled-enrollment design was effective at minimizing
participant loss due to attrition and reducing the risk of testing
effects due to repeated measurements.Study two was an
efficiency-type PMD replication of Infante and Wigley's (1986)
verbal aggressiveness scale validation study, in which each
participant was presented with only 95 items from the 147 item
survey instrument. Through the use of an efficiency design, this
study was able to replicate the results of the original study with
a dramatically reduced time burden on the participants, indicating
that efficiency-type PMDs are an effective tool for scale
shortening.Study three was an accelerated longitudinal PMD
replication of Rubin, Graham, and Mignerey's (1990) longitudinal
communication competence study, which measured change in students'
communication competence over the course of a college career.
Through the use of an accelerated longitudinal PMD, data collection
was completed in just over one calendar year, far shorter than the
three years the original study took to collect data. A flaw in
participant retention procedures prevented data analysis from being
conducted, but this study did effectively illustrate the increased
methodological complexities caused by PMDs.This dissertation
concludes that PMDs can be of substantial benefit to communication
research, and should be adopted in the discipline. Special
attention must be paid, however, to the increased design complexity
added by the use of these methods.
Advisors/Committee Members: Chadwick, Amy (Advisor).
Subjects/Keywords: Communication; missing data; planned missing data; longitudinal; multiple imputation; communication; communication research
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APA (6th Edition):
Parsons, M. M. (2013). Planned Missing Data Designs in Communication
Research. (Doctoral Dissertation). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1376579676
Chicago Manual of Style (16th Edition):
Parsons, Michael M. “Planned Missing Data Designs in Communication
Research.” 2013. Doctoral Dissertation, Ohio University. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1376579676.
MLA Handbook (7th Edition):
Parsons, Michael M. “Planned Missing Data Designs in Communication
Research.” 2013. Web. 11 Dec 2019.
Vancouver:
Parsons MM. Planned Missing Data Designs in Communication
Research. [Internet] [Doctoral dissertation]. Ohio University; 2013. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1376579676.
Council of Science Editors:
Parsons MM. Planned Missing Data Designs in Communication
Research. [Doctoral Dissertation]. Ohio University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1376579676
97.
Moghaddam, Shirin.
Bayesian imputation of right censored data in time-to-event studies.
Degree: 2018, NUI Galway
URL: http://hdl.handle.net/10379/7257
► In time-to-event studies subjects are followed until the event of interest has happened. Subjects who do not experience the event are referred to as censored.…
(more)
▼ In time-to-event studies subjects are followed until the event of interest has happened.
Subjects who do not experience the event are referred to as censored. Due
to censoring, methods of plotting individual survival time, such as density plots,
are invalid. The graphical displays of time-to-event data usually take the form of
a Kaplan-Meier survival plot. However, using a Kaplan-Meier survival plot might
not be the most informative way to present the data to answer the typical questions
of interest. The median survival is often used as a summary of the survival experience
of a patients' population and it is easily read of the Kaplan-Meier plot. It
is unlikely however that the median is a relevant summary at the patient level and
a density plot of the data is perhaps more informative for communication than a
single summary statistic. A fundamental idea in this thesis is to consider censored
data as a form of missing, incomplete, data and use approaches from the missing
data literature to handle this issue. In particular, we will use the idea of imputing
the censored observations, based on the other information in the dataset and
some form of assumed model. By imputing values for the censored observations and
combining the original complete and imputed incomplete data, it is possible to plot
the density of the full data to complement the information given by Kaplan-Meier
plots. In this thesis, we consider using parametric Bayesian and non-parametric
Bayesian methods to impute right censored survival data to achieve this aim. The
imputation of censored observations not only allows more interpretable graphics to
be produced for a wider general audience (physicians and patients), but it opens
up the possibility of the use of standard formal methods of analysis for continuous
responses.
2020-03-20
Advisors/Committee Members: Hinde, John, Newell, John, Irish Research Council for Science, Engineering and Technology.
Subjects/Keywords: Survival analysis; Bayesian analysis; Censored data; Imputation; Mathematics, Statistics and Applied Mathematics; Statistics
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APA (6th Edition):
Moghaddam, S. (2018). Bayesian imputation of right censored data in time-to-event studies. (Thesis). NUI Galway. Retrieved from http://hdl.handle.net/10379/7257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Moghaddam, Shirin. “Bayesian imputation of right censored data in time-to-event studies.” 2018. Thesis, NUI Galway. Accessed December 11, 2019.
http://hdl.handle.net/10379/7257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Moghaddam, Shirin. “Bayesian imputation of right censored data in time-to-event studies.” 2018. Web. 11 Dec 2019.
Vancouver:
Moghaddam S. Bayesian imputation of right censored data in time-to-event studies. [Internet] [Thesis]. NUI Galway; 2018. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/10379/7257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Moghaddam S. Bayesian imputation of right censored data in time-to-event studies. [Thesis]. NUI Galway; 2018. Available from: http://hdl.handle.net/10379/7257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Georgia State University
98.
Alemdar, Meltem.
A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models.
Degree: PhD, Educational Policy Studies, 2009, Georgia State University
URL: https://scholarworks.gsu.edu/eps_diss/34
► Unlike multilevel data with a purely nested structure, data that are cross-classified not only may be clustered into hierarchically ordered units but also may belong…
(more)
▼ Unlike multilevel data with a purely nested structure, data that are cross-classified not only may be clustered into hierarchically ordered units but also may belong to more than one unit at a given level of a hierarchy. In a cross-classified design, students at a given school might be from several different neighborhoods and one neighborhood might have students who attend a number of different schools. In this type of scenario, schools and neighborhoods are considered to be cross-classified factors, and cross-classified random effects modeling (CCREM) should be used to analyze these data appropriately. A common problem in any type of multilevel analysis is the presence of missing data at any given level. There has been little research conducted in the multilevel literature about the impact of missing data, and none in the area of cross-classified models. The purpose of this study was to examine the effect of data that are missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), on CCREM estimates while exploring multiple
imputation to handle the missing data. In addition, this study examined the impact of including an auxiliary variable that is correlated with the variable with missingness (the level-1 predictor) in the
imputation model for multiple
imputation. This study expanded on the CCREM Monte Carlo simulation work of Meyers (2004) by the inclusion of studying the effect of missing data and method for handling these missing data with CCREM. The results demonstrated that in general, multiple
imputation met Hoogland and Boomsma’s (1998) relative bias estimation criteria (less than 5% in magnitude) for parameter estimates under different types of missing data patterns. For the standard error estimates, substantial relative bias (defined by Hoogland and Boomsma as greater than 10%) was found in some conditions. When multiple
imputation was used to handle the missing data then substantial bias was found in the standard errors in most cells where data were MNAR. This bias increased as a function of the percentage of missing data.
Advisors/Committee Members: Carolyn F. Furlow - Chair, Philo A. Hutcheson, Sheryl A. Gowen, Phillip E. Gagne.
Subjects/Keywords: Cross- Classified Data; Cross-Classified Random Effects Models; Missing Data; Multiple Imputation; Education; Education Policy
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APA (6th Edition):
Alemdar, M. (2009). A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/eps_diss/34
Chicago Manual of Style (16th Edition):
Alemdar, Meltem. “A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models.” 2009. Doctoral Dissertation, Georgia State University. Accessed December 11, 2019.
https://scholarworks.gsu.edu/eps_diss/34.
MLA Handbook (7th Edition):
Alemdar, Meltem. “A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models.” 2009. Web. 11 Dec 2019.
Vancouver:
Alemdar M. A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models. [Internet] [Doctoral dissertation]. Georgia State University; 2009. [cited 2019 Dec 11].
Available from: https://scholarworks.gsu.edu/eps_diss/34.
Council of Science Editors:
Alemdar M. A Monte Carlo Study: The Impact of Missing Data in Cross-Classification Random Effects Models. [Doctoral Dissertation]. Georgia State University; 2009. Available from: https://scholarworks.gsu.edu/eps_diss/34
University of Bradford
99.
Mohd Jamil, J. B.
Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods.
Degree: PhD, 2012, University of Bradford
URL: http://hdl.handle.net/10454/5728
► Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged…
(more)
▼ Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of data imputation and computational intelligence methods of data imputation. Due to the longstanding use of statistical methods in handling missing data problems, it takes quite some time for computational intelligence methods to gain profound attention even though these methods have analogous accuracy, in comparison to other approaches. The merits of both these classes have been discussed at length in the literature, but only limited studies make significant comparison to these classes. This thesis contributes to knowledge by firstly, conducting a comprehensive comparison of standard statistical methods of data imputation, namely, mean substitution (MS), regression imputation (RI), expectation maximization (EM), tree imputation (TI) and multiple imputation (MI) on missing completely at random (MCAR) data sets. Secondly, this study also compares the efficacy of these methods with a computational intelligence method of data imputation, ii namely, a neural network (NN) on missing not at random (MNAR) data sets. The significance difference in performance of the methods is presented. Thirdly, a novel procedure for handling missing data is presented. A hybrid combination of each of these statistical methods with a NN, known here as the post-processing procedure, was adopted to approximate MNAR data sets. Simulation studies for each of these imputation approaches have been conducted to assess the impact of missing values on partial least squares structural equation modelling (PLS-SEM) based on the estimated accuracy of both structural and measurement parameters. The best method to deal with particular missing data mechanisms is highly recognized. Several significant insights were deduced from the simulation results. It was figured that for the problem of MCAR by using statistical methods of data imputation, MI performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the NN post-processing procedure. This improvement in accuracy may be resulted from the neural network's ability to derive meaning from the imputed data set found by the statistical methods. Based on these results, the NN post-processing procedure is capable to assist MS in producing significant improvement in accuracy of the approximated values. This is a promising result, as MS is the weakest method in this study. This evidence is also informative as MS is often used as the default method available to users of PLS-SEM software.
Subjects/Keywords: 658; Missing data; Partial least squares; Structural equation modelling; Neural networks; Imputation methods; Incomplete data
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APA (6th Edition):
Mohd Jamil, J. B. (2012). Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods. (Doctoral Dissertation). University of Bradford. Retrieved from http://hdl.handle.net/10454/5728
Chicago Manual of Style (16th Edition):
Mohd Jamil, J B. “Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods.” 2012. Doctoral Dissertation, University of Bradford. Accessed December 11, 2019.
http://hdl.handle.net/10454/5728.
MLA Handbook (7th Edition):
Mohd Jamil, J B. “Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods.” 2012. Web. 11 Dec 2019.
Vancouver:
Mohd Jamil JB. Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods. [Internet] [Doctoral dissertation]. University of Bradford; 2012. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/10454/5728.
Council of Science Editors:
Mohd Jamil JB. Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods. [Doctoral Dissertation]. University of Bradford; 2012. Available from: http://hdl.handle.net/10454/5728
KTH
100.
Jalil, Walid Abdul.
The impact of missing data imputation on HCC survival prediction : Exploring the combination of missing data imputation with data-level methods such as clustering and oversampling.
Degree: Engineering Sciences (SCI), 2018, KTH
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230741
► The area of data imputation, which is the process of replacing missing data with substituted values, has been covered quite extensively in recent years.…
(more)
▼ The area of data imputation, which is the process of replacing missing data with substituted values, has been covered quite extensively in recent years. The literature on the practical impact of data imputation however, remains scarce. This thesis explores the impact of some of the state of the art data imputation methods on HCC survival prediction and classification in combination with data-level methods such as oversampling. More specifically, it explores imputation methods for mixed-type datasets and their impact on a particular HCC dataset. Previous research has shown that, the newer, more sophisticated imputation methods outperform simpler ones when evaluated with normalized root mean square error (NRMSE). Contrary to intuition however, the results of this study show that when combined with other data-level methods such as clustering and oversampling, the differences in imputation performance does not always impact classification in any meaningful way. This might be explained by the noise that is introduced when generating synthetic data points in the oversampling process. The results also show that one of the more sophisticated imputation methods, namely MICE, is highly dependent on prior assumptions about the underlying distributions of the dataset. When those assumptions are incorrect, the imputation method performs poorly and has a considerable negative impact on classification.
Subjects/Keywords: missing data; imputation; HCC; survival prediction; oversampling; Engineering and Technology; Teknik och teknologier
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APA (6th Edition):
Jalil, W. A. (2018). The impact of missing data imputation on HCC survival prediction : Exploring the combination of missing data imputation with data-level methods such as clustering and oversampling. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230741
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Jalil, Walid Abdul. “The impact of missing data imputation on HCC survival prediction : Exploring the combination of missing data imputation with data-level methods such as clustering and oversampling.” 2018. Thesis, KTH. Accessed December 11, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230741.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jalil, Walid Abdul. “The impact of missing data imputation on HCC survival prediction : Exploring the combination of missing data imputation with data-level methods such as clustering and oversampling.” 2018. Web. 11 Dec 2019.
Vancouver:
Jalil WA. The impact of missing data imputation on HCC survival prediction : Exploring the combination of missing data imputation with data-level methods such as clustering and oversampling. [Internet] [Thesis]. KTH; 2018. [cited 2019 Dec 11].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230741.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jalil WA. The impact of missing data imputation on HCC survival prediction : Exploring the combination of missing data imputation with data-level methods such as clustering and oversampling. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230741
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Université Catholique de Louvain
101.
Pholo Bala, Alain.
Rent seeking and hubs : towards a new economic geography of sub saharan africa.
Degree: 2009, Université Catholique de Louvain
URL: http://hdl.handle.net/2078.1/22720
► This thesis is a modest attempt to uncover the main features and the underlying drives of Sub Saharan urbanization process. We begin by describing the…
(more)
▼ This thesis is a modest attempt to uncover the main features and the underlying drives of Sub Saharan urbanization process. We begin by describing the stylized facts of urbanization in that region. Sub Saharan Africa’s urban pattern features urban bias and urban primacy. Locational advantages and political effects have induced a bias in favor of political capitals and ports. Thus, literature seems to emphasize rent-seeking and hubs determinants at the expense of agglomeration economies strengthening the prejudice against Sub Saharan Africa biggest cities.
We question that preconception by performing an empirical analysis investigating the relationship between urban concentration and economic growth. The novelty of our approach lies in the use of semiparametric methods along with a missing data imputation algorithm.
Our empirical results point out the specificity of the urbanization patterns in different regions. This prompts us to better characterize the underlying drives of Sub-Saharan Africa urbanization process. This is the challenge of our theoretical investigation which is twofold. On one hand we have attempted to explain urban agglomeration in Sub Saharan Africa by both economic and political factors. We find that this rent-seeking behavior fuels the formation of large urban agglomerations in developing countries, via mechanisms of interregional income transfers. Such a finding may serve to explain a seemingly paradoxical aspect of urban development in SSA: agglomeration despite high trade costs.
On another hand, we rely on international trade and hub effects to explain the formation of agglomerations. We are thus able to explain the evidence of the persistence and the fostering of huge agglomerations in coastal locations of Sub-Saharan Africa. We find that openness is likely to trigger agglomeration in the hub especially when transport costs are low. This result is consistent with Weber’s theory of location (Beckmann and Thisse, 1986) which states that in a star-shaped network without any dominant location, entry points are the optimal locations.
(ECON 3) – UCL, 2009
Advisors/Committee Members: UCL - ESPO/ECON - Département des sciences économiques, Thisse, Jacques-François, Peeters, Dominique, Weiserbs, Daniel, Bauwens, Luc, Behrens, Kristian, Bertinelli, Luisito.
Subjects/Keywords: Urban primacy; Economic growth; Missing data imputation; Semiparametric estimation; Economic geography; Rent-seeking; Hub
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APA (6th Edition):
Pholo Bala, A. (2009). Rent seeking and hubs : towards a new economic geography of sub saharan africa. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/22720
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Pholo Bala, Alain. “Rent seeking and hubs : towards a new economic geography of sub saharan africa.” 2009. Thesis, Université Catholique de Louvain. Accessed December 11, 2019.
http://hdl.handle.net/2078.1/22720.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Pholo Bala, Alain. “Rent seeking and hubs : towards a new economic geography of sub saharan africa.” 2009. Web. 11 Dec 2019.
Vancouver:
Pholo Bala A. Rent seeking and hubs : towards a new economic geography of sub saharan africa. [Internet] [Thesis]. Université Catholique de Louvain; 2009. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/2078.1/22720.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Pholo Bala A. Rent seeking and hubs : towards a new economic geography of sub saharan africa. [Thesis]. Université Catholique de Louvain; 2009. Available from: http://hdl.handle.net/2078.1/22720
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
The Ohio State University
102.
Kline, David.
Systematically Missing Subject-Level Data in Longitudinal
Research Synthesis.
Degree: PhD, Biostatistics, 2015, The Ohio State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809
► When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data.…
(more)
▼ When conducting research synthesis, the collection of
studies that will be combined often do not measure the same set of
variables, which creates missing data. When the studies to combine
are longitudinal, missing data can occur on either the
observation-level (time-varying) or the
subject-level
(non-time-varying). Traditionally, the focus of missing data
methods for longitudinal data has been on missing observation-level
variables. In this dissertation, we focus on missing
subject-level
variables where few methods have been developed or compared. We
compare two multiple
imputation approaches that have been proposed
for missing
subject-level data in single longitudinal studies: a
joint modeling approach and a sequential conditional modeling
approach. Based on analytical and empirical results for the case
when all variables are normally distributed, we find the joint
modeling approach to be preferable to the sequential conditional
approach except when the covariance structure of the repeated
outcome for each individual has homogenous variance and
exchangeable correlation. Specifically, the regression coefficient
estimates from an analysis incorporating imputed values based on
the sequential conditional method are attenuated and less efficient
than those from the joint method. Based on this preference, we
develop a new joint model for multiple
imputation of missing
subject-level variables that models
subject- and observation-level
variables with distributions in the exponential family. Our model
is built within the generalized linear models framework and uses
normally distributed latent variables to account for dependence on
both the
subject- and observation-levels. When compared via
simulation, the performance of our model is similar to or better
than existing approaches for imputing missing
subject-level
variables with normal, Bernoulli, Poisson, and multinomial
distributions. We illustrate our method by applying it to combine
two longitudinal studies on the psychological and social effects of
pediatric traumatic brain injury that have systematically missing
subject-level data.
Advisors/Committee Members: Andridge, Rebbeca (Advisor), Kaizar, Eloise (Advisor).
Subjects/Keywords: Biostatistics; Statistics; multiple imputation; research synthesis; longitudinal data; missing data; individual patient data
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kline, D. (2015). Systematically Missing Subject-Level Data in Longitudinal
Research Synthesis. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809
Chicago Manual of Style (16th Edition):
Kline, David. “Systematically Missing Subject-Level Data in Longitudinal
Research Synthesis.” 2015. Doctoral Dissertation, The Ohio State University. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809.
MLA Handbook (7th Edition):
Kline, David. “Systematically Missing Subject-Level Data in Longitudinal
Research Synthesis.” 2015. Web. 11 Dec 2019.
Vancouver:
Kline D. Systematically Missing Subject-Level Data in Longitudinal
Research Synthesis. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809.
Council of Science Editors:
Kline D. Systematically Missing Subject-Level Data in Longitudinal
Research Synthesis. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809
Ohio University
103.
Hening, Dyah A.
Missing Data Imputation Method Comparison in Ohio University
Student Retention Database.
Degree: MS, Industrial and Systems Engineering (Engineering and
Technology), 2009, Ohio University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1256491596
► Ohio University has been conducting research on first-year-student retention to prevent dropouts (OU Office of Institutional Research, First-Year Students Retention, 2008). Yet, the data sets…
(more)
▼ Ohio University has been conducting research on
first-year-student retention to prevent dropouts (OU Office of
Institutional Research, First-Year Students Retention, 2008). Yet,
the data sets have more than 20% of missing values, which can lead
to bias in prediction. Missing data affects on the ability to
generalize results to the target population. This study categorizes
the missing data in variables into one of three types of missing
data: missing completely at random (MCAR), missing at random (MAR),
or missing not at random (MNAR). After the missing data is
identified, the proper method of handling it is discussed. The
proposed method is validated through developed and tested models.
The goal of this work is to explore the methods of
imputation
missing data, and apply them to the Ohio University student
retention dataset.
Advisors/Committee Members: Koonce, David (Advisor).
Subjects/Keywords: Higher Education; Industrial Engineering; Data Imputation; Missing data; MNAR; MCAR; MAR; student retention
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hening, D. A. (2009). Missing Data Imputation Method Comparison in Ohio University
Student Retention Database. (Masters Thesis). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1256491596
Chicago Manual of Style (16th Edition):
Hening, Dyah A. “Missing Data Imputation Method Comparison in Ohio University
Student Retention Database.” 2009. Masters Thesis, Ohio University. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1256491596.
MLA Handbook (7th Edition):
Hening, Dyah A. “Missing Data Imputation Method Comparison in Ohio University
Student Retention Database.” 2009. Web. 11 Dec 2019.
Vancouver:
Hening DA. Missing Data Imputation Method Comparison in Ohio University
Student Retention Database. [Internet] [Masters thesis]. Ohio University; 2009. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1256491596.
Council of Science Editors:
Hening DA. Missing Data Imputation Method Comparison in Ohio University
Student Retention Database. [Masters Thesis]. Ohio University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1256491596
Cleveland State University
104.
Brown, Marvin Lane.
The Impact of Data Imputation Methodologies on Knowledge
Discovery.
Degree: Doctor of Business Administration, Nance College of Business Administration, 2008, Cleveland State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769
► The purpose of this research is to investigate the impact of Data Imputation Methodologies that are employed when a specific Data Mining algorithm is…
(more)
▼ The purpose of this research is to
investigate the impact of Data
Imputation Methodologies that are
employed when a specific Data Mining algorithm is utilized within a
KDD (Knowledge Discovery in Databases) process. This study will
employ certain Knowledge Discovery processes that are widely
accepted in both the academic and commercial worlds. Several
Knowledge Discovery models will be developed utilizing secondary
data containing known correct values. Tests will be conducted on
the secondary data both before and after storing data instances
with known results and then identifying imprecise data values. One
of the integral stages in the accomplishment of successful
Knowledge Discovery is the Data Mining phase. The actual Data
Mining process deals significantly with prediction, estimation,
classification, pattern recognition and the development of
association rules. Neural Networks are the most commonly selected
tools for Data Mining classification and prediction. Neural
Networks employ various types of Transfer Functions when outputting
data. The most commonly employed Transfer Function is the s-Sigmoid
Function. Various Knowledge Discovery Models from various research
and business disciplines were tested using this
framework. However, missing and inconsistent data
has been pervasive problems in the history of data analysis since
the origin of data collection. Due to advancements in the
capacities of data storage and the proliferation of computer
software, more historical data is being collected and analyzed
today than ever before. The issue of missing data must be
addressed, since ignoring this problem can introduce bias into the
models being evaluated and lead to inaccurate data mining
conclusions. The objective of this research is to address the
impact of Missing Data and Data
Imputation on the Data Mining phase
of Knowledge Discovery when Neural Networks are utilized when
employing an s-Sigmoid Transfer function, and are confronted with
Missing Data and Data
Imputation
methodologies.
Advisors/Committee Members: Lin, Chien-Hua (Mike) (Committee Chair), Lynn, Marc (Advisor).
Subjects/Keywords: Business Education; Computer Science; Data Mining; Knowledge Discovery; Data Imputation; Neural Networks; Transfer Functions; Sigmoid
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Brown, M. L. (2008). The Impact of Data Imputation Methodologies on Knowledge
Discovery. (Doctoral Dissertation). Cleveland State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769
Chicago Manual of Style (16th Edition):
Brown, Marvin Lane. “The Impact of Data Imputation Methodologies on Knowledge
Discovery.” 2008. Doctoral Dissertation, Cleveland State University. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769.
MLA Handbook (7th Edition):
Brown, Marvin Lane. “The Impact of Data Imputation Methodologies on Knowledge
Discovery.” 2008. Web. 11 Dec 2019.
Vancouver:
Brown ML. The Impact of Data Imputation Methodologies on Knowledge
Discovery. [Internet] [Doctoral dissertation]. Cleveland State University; 2008. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769.
Council of Science Editors:
Brown ML. The Impact of Data Imputation Methodologies on Knowledge
Discovery. [Doctoral Dissertation]. Cleveland State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769
Penn State University
105.
Roth, Veronica Leigh.
Detecting & Adjusting for Attrition Bias in Longitudinal
Survey Panels.
Degree: PhD, Sociology, 2015, Penn State University
URL: https://etda.libraries.psu.edu/catalog/26344
► The use of panel studies, in which the same people are interviewed at least twice in two different time periods, provides researchers with the ability…
(more)
▼ The use of panel studies, in which the same people are
interviewed at least twice in two different time periods, provides
researchers with the ability to explore a multitude of issues.
Researchers can explore change over time, better establish temporal
order of events, and have the option to use a wide array of models
that are only possible with longitudinal survey data (Johnson,
1988; Toon, 2000). With more waves of data, however, comes the
potential for more problems in data collection and analysis.
Researchers may feel pressured to choose between question
consistency and updating surveys for contemporary language or
issues (Olsen, 2005). For my dissertation, I explore how the
detection and correction of attrition may be performed after data
is collected. Although the prevention of attrition in the
collection phase may be ideal, the proliferation of large and
widely available datasets from the government and academia means
that a considerable amount of research is performed by those
without any ability to impact data collection. Given the rise of
computing power and the increasing knowledge of multiple imputation
and complex statistical models, such as fixed and random effects,
today’s researcher may have an increased ability to perform
necessary adjustments for the problems that plague panel studies.
The central research question for this dissertation is: Given the
presence of attrition, and the implications of attrition for biased
estimates, what procedures will aid researchers in estimating valid
findings?
Subjects/Keywords: attrition; panel conditioning; refreshment panels;
multiple imputation; longitudinal panel data; marital
quality
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APA ·
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MLA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Roth, V. L. (2015). Detecting & Adjusting for Attrition Bias in Longitudinal
Survey Panels. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26344
Chicago Manual of Style (16th Edition):
Roth, Veronica Leigh. “Detecting & Adjusting for Attrition Bias in Longitudinal
Survey Panels.” 2015. Doctoral Dissertation, Penn State University. Accessed December 11, 2019.
https://etda.libraries.psu.edu/catalog/26344.
MLA Handbook (7th Edition):
Roth, Veronica Leigh. “Detecting & Adjusting for Attrition Bias in Longitudinal
Survey Panels.” 2015. Web. 11 Dec 2019.
Vancouver:
Roth VL. Detecting & Adjusting for Attrition Bias in Longitudinal
Survey Panels. [Internet] [Doctoral dissertation]. Penn State University; 2015. [cited 2019 Dec 11].
Available from: https://etda.libraries.psu.edu/catalog/26344.
Council of Science Editors:
Roth VL. Detecting & Adjusting for Attrition Bias in Longitudinal
Survey Panels. [Doctoral Dissertation]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26344
UCLA
106.
Yi, Yi.
A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations.
Degree: Statistics, 2014, UCLA
URL: http://www.escholarship.org/uc/item/94k5p5mx
► Repeated-Measures longitudinal data is common in drug research, where every patient is repeatedly measured across time. Responses could either be continuous variables such as blood…
(more)
▼ Repeated-Measures longitudinal data is common in drug research, where every patient is repeatedly measured across time. Responses could either be continuous variables such as blood pressure or binary variables such as drug test positive/negative. Issues to be addressed are within-subject observation dependence, as well as the between-subject differences (mixed effects). Another important problem to address is the missingness and dropouts.Full likelihood-based models such as the generalized linear mixed model (GLMM) together with EM algorithm could be utilized, given simplified parametric correlation structure between random components. If the interest is only the mean parameters, little in subject effects, the non-likelihood-based generalized estimating equations (GEE) is a good alternative. GEE circumvents the structural and computational complexities of likelihood-based models, and it is robust to misspecification of the working correlation structures of marginal observations. However, as a non-likelihood frequentist marginal model, GEE itself lacks strength dealing with missing data mechanism beyond missing completely at random (MCAR). Therefore, integration of multiple imputation and GEE (MI-GEE) is a great solution to longitudinal data with dropouts.Real data application is performed on MI-GEE and GLMM. Our results for the bupropion study dataset show effective but not significant strength of Bupropion for treating methamphetamine dependence, which is consistent with previous studies and biological sense.
Subjects/Keywords: Statistics; Pharmaceutical sciences; Biostatistics; Bupropion; Generalized Estimating Equations; Longitudinal Data; Multiple Imputation; Repeated Measures
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APA (6th Edition):
Yi, Y. (2014). A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/94k5p5mx
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yi, Yi. “A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations.” 2014. Thesis, UCLA. Accessed December 11, 2019.
http://www.escholarship.org/uc/item/94k5p5mx.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yi, Yi. “A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations.” 2014. Web. 11 Dec 2019.
Vancouver:
Yi Y. A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Dec 11].
Available from: http://www.escholarship.org/uc/item/94k5p5mx.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yi Y. A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/94k5p5mx
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
UCLA
107.
RIZZO VARELA, SHEMRA.
Uncertainty in Meta-Analysis: Bridging the Divide Between Ideal and Available Extracted Data.
Degree: Biostatistics, 2015, UCLA
URL: http://www.escholarship.org/uc/item/1qg250sq
► Meta-analysis in the health sciences combines evidence from multiple studies to derive stronger conclusions about the efficacy of treatments. In the process of data extraction…
(more)
▼ Meta-analysis in the health sciences combines evidence from multiple studies to derive stronger conclusions about the efficacy of treatments. In the process of data extraction from published papers, it is extremely common for the required data to be ambiguous, incomplete or missing. We consider the case of meta-analysis of odds-ratios with unknown number of events and meta-analysis of mean differences with missing standard errors. Existing approaches consist of computing best-estimates for the missing values then feeding them into the meta-analysis as extracted data without accounting for the uncertainty of the computations. These naive approaches lead to over-certain results and potentially inaccurate conclusions. Meta-analysis of odds-ratios assumes binomially distributed numbers of events in each treatment group and requires extracted number of events, which are often not available due to loss to follow-up. Common practice consists of inferring the probability of survival from measurements of the Kaplan Meier survival plot and then using it to infer the number of deaths. We propose the Uncertain Reading-Estimated Events model to construct each study's contribution to the meta-analysis separately using the data available for extraction. In our meta-analysis comparing CABG and PCI for ULMCA stenosis, accounting for the uncertainty results in increased standard deviations of the log-odds as compared to a naive meta-analysis that assumes ideal extracted data, equivalent to a reduction of the overall sample size of 43% in our example. Simulations show that meta-analysis based on the observed number of deaths lead to biased estimates while our model does not. Meta-analysis of mean differences requires extracted mean differences and their standard errors (SE). However, missing standard errors are pervasive in publications. An algebraic computation to recover the missing SE utilizes the baseline and follow-up standard deviations, and correlations, which are also typically missing. Traditional approaches, that have not been theoretically derived, replace missing SEs with various single-value imputations. We formally derive the Uncertain Standard Error Bayesian model to accommodate multiple patterns of missingness in the standard deviations. In our meta-analysis comparing home monitoring blood pressure to usual care, accounting for the uncertainty results in larger posterior SEs compared to the traditional approaches.
Subjects/Keywords: Biostatistics; Statistics; Bayesian modeling; censoring; imputation; mean difference; missing data; odds ratios
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APA ·
Chicago ·
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APA (6th Edition):
RIZZO VARELA, S. (2015). Uncertainty in Meta-Analysis: Bridging the Divide Between Ideal and Available Extracted Data. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/1qg250sq
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
RIZZO VARELA, SHEMRA. “Uncertainty in Meta-Analysis: Bridging the Divide Between Ideal and Available Extracted Data.” 2015. Thesis, UCLA. Accessed December 11, 2019.
http://www.escholarship.org/uc/item/1qg250sq.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
RIZZO VARELA, SHEMRA. “Uncertainty in Meta-Analysis: Bridging the Divide Between Ideal and Available Extracted Data.” 2015. Web. 11 Dec 2019.
Vancouver:
RIZZO VARELA S. Uncertainty in Meta-Analysis: Bridging the Divide Between Ideal and Available Extracted Data. [Internet] [Thesis]. UCLA; 2015. [cited 2019 Dec 11].
Available from: http://www.escholarship.org/uc/item/1qg250sq.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
RIZZO VARELA S. Uncertainty in Meta-Analysis: Bridging the Divide Between Ideal and Available Extracted Data. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/1qg250sq
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
UCLA
108.
Peng, Qin.
Trees vs Neurons: Comparison between Denoising Autoencoders and Random Forest for Imputation of Mixed Data from Electronic Medical Records.
Degree: Statistics, 2018, UCLA
URL: http://www.escholarship.org/uc/item/4tp3b2bt
► Missing data is a significant challenge impacting almost all studies; however, this is especially true for analyses of electronic health record (EHR). We propose a…
(more)
▼ Missing data is a significant challenge impacting almost all studies; however, this is especially true for analyses of electronic health record (EHR). We propose a multiple imputation model based on multi-layer denoising autoencoders. This nonparametric model can deal with mixed-typed data types, and not making assumptions of missing mechanism. Evaluation on simulated datasets based on real life EHR datasets showed that our proposed model outperforms current Random Forest method and median/mode Imputation.
Subjects/Keywords: Statistics; Denoising autoencoder; electronic health records; imputation; mixed-typed data; nonparametric model
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APA ·
Chicago ·
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Manager
APA (6th Edition):
Peng, Q. (2018). Trees vs Neurons: Comparison between Denoising Autoencoders and Random Forest for Imputation of Mixed Data from Electronic Medical Records. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/4tp3b2bt
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Peng, Qin. “Trees vs Neurons: Comparison between Denoising Autoencoders and Random Forest for Imputation of Mixed Data from Electronic Medical Records.” 2018. Thesis, UCLA. Accessed December 11, 2019.
http://www.escholarship.org/uc/item/4tp3b2bt.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Peng, Qin. “Trees vs Neurons: Comparison between Denoising Autoencoders and Random Forest for Imputation of Mixed Data from Electronic Medical Records.” 2018. Web. 11 Dec 2019.
Vancouver:
Peng Q. Trees vs Neurons: Comparison between Denoising Autoencoders and Random Forest for Imputation of Mixed Data from Electronic Medical Records. [Internet] [Thesis]. UCLA; 2018. [cited 2019 Dec 11].
Available from: http://www.escholarship.org/uc/item/4tp3b2bt.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Peng Q. Trees vs Neurons: Comparison between Denoising Autoencoders and Random Forest for Imputation of Mixed Data from Electronic Medical Records. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/4tp3b2bt
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Tennessee – Knoxville
109.
Yang, Jianjiang.
SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC.
Degree: 2015, University of Tennessee – Knoxville
URL: https://trace.tennessee.edu/utk_graddiss/3315
► Short-term traffic forecasting and missing data imputation can benefit from the use of neighboring traffic information, in addition to temporal data alone. However, little attention…
(more)
▼ Short-term traffic forecasting and missing data imputation can benefit from the use of neighboring traffic information, in addition to temporal data alone. However, little attention has been given to quantifying the effect of upstream and downstream traffic on the traffic at current location. The knowledge about temporal and spatial propagation of traffic is still limited in the current literature. To fill this gap, this dissertation research focus on revealing the spatio-temporal correlations between neighboring traffic to develop reliable algorithms for short-term traffic forecasting and data imputation based on spatio-temporal dynamics of traffic.
In the first part of this dissertation, spatio-temporal relationships of speed series from consecutive segments were studied for different traffic conditions. The analysis results show that traffic speeds of consecutive segments are highly correlated. While downstream traffic tends to replicate the upstream condition under light traffic conditions, it may also affect upstream condition during congestion and build up situations. These effects were statistically quantified and an algorithm for properly choosing the “best” or most correlated neighbor(s), for potential traffic prediction or imputation purposes was proposed.
In the second part of the dissertation, a spatio-temporal kriging (ST-Kriging) model that determines the most desirable extent of spatial and temporal traffic data from neighboring locations was developed for short-term traffic forecasting. The new ST-Kriging model outperforms all benchmark models under various traffic conditions.
In the final part of the dissertation, a spatio-temporal data imputation approach was proposed and its performance was evaluated under scenarios with different data missing rates. Compared against previous methods, better flexibility and stable imputation accuracy were reported for this new imputation technique.
Subjects/Keywords: short-term traffic forecasting; spatio-temporal correlation; data imputation; kriging; self-learning; Civil Engineering
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APA ·
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Export
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APA (6th Edition):
Yang, J. (2015). SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/3315
Chicago Manual of Style (16th Edition):
Yang, Jianjiang. “SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC.” 2015. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed December 11, 2019.
https://trace.tennessee.edu/utk_graddiss/3315.
MLA Handbook (7th Edition):
Yang, Jianjiang. “SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC.” 2015. Web. 11 Dec 2019.
Vancouver:
Yang J. SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2015. [cited 2019 Dec 11].
Available from: https://trace.tennessee.edu/utk_graddiss/3315.
Council of Science Editors:
Yang J. SPATIO-TEMPORAL DYNAMICS OF SHORT-TERM TRAFFIC. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2015. Available from: https://trace.tennessee.edu/utk_graddiss/3315
University of Edinburgh
110.
Corbin, Laura Jayne.
Application of genomic technologies to the horse.
Degree: PhD, 2013, University of Edinburgh
URL: http://hdl.handle.net/1842/11808
► The publication of a draft equine genome sequence and the release by Illumina of a 50,000 marker single-nucleotide polymorphism (SNP) genotyping chip has provided equine…
(more)
▼ The publication of a draft equine genome sequence and the release by Illumina of a 50,000 marker single-nucleotide polymorphism (SNP) genotyping chip has provided equine researchers with the opportunity to use new approaches to study the relationships between genotype and phenotype. In particular, it is hoped that the use of high-density markers applied to population samples will enable progress to be made with regard to more complex diseases. The first objective of this thesis is to explore the potential for the equine SNP chip to enable such studies to be performed in the horse. The second objective is to investigate the genetic background of osteochondrosis (OC) in the horse. These objectives have been tackled using 348 Thoroughbreds from the US, divided into cases and controls, and a further 836 UK Thoroughbreds, the majority with no phenotype data. All horses had been genotyped with the Illumina Equine SNP50 BeadChip. Linkage disequilibrium (LD) is the non-random association of alleles at neighbouring loci. The reliance of many genomic methodologies on LD between neutral markers and causal variants makes it an important characteristic of genome structure. In this thesis, the genomic data has been used to study the extent of LD in the Thoroughbred and the results considered in terms of genome coverage. Results suggest that the SNP chip offers good coverage of the genome. Published theoretical relationships between LD and historical effective population size (Ne) were exploited to enable accuracy predictions for genome-wide evaluation (GWE) to be made. A subsequent in-depth exploration of this theory cast some doubt on the reliability of this approach in the estimation of Ne, but the general conclusion that the Thoroughbred population has a small Ne which should enable GWE to be carried out efficiently in this population, remains valid. In the course of these studies, possible errors embedded within the current sequence assembly were identified using empirical approaches. Osteochondrosis is a developmental orthopaedic disease which affects the joints of young horses. Osteochondrosis is considered multifactorial in origin with a variety of environmental factors and heredity having been implicated. In this thesis, a genome-wide association study was carried out to identify quantitative trait loci (QTL) associated with OC. A single SNP was found to be significantly associated with OC. The low heritability of OC combined with the apparent lack of major QTL suggests GWE as an alternative approach to tackle this disease. A GWE analysis was carried out on the same dataset but the resulting genomic breeding values had no predictive ability for OC status. This, combined with the small number of significant QTL, indicates a lack of power which could be addressed in the future by increasing sample size. An alternative to genotyping more horses for the 50K SNP chip would be to use a low-density SNP panel and impute remaining genotypes. The final chapter of this thesis examines the feasibility of this approach in the…
Subjects/Keywords: 636.1; equine; genome selection; genome-wide association studies; GWAS; imputation; linkage disequilibrium; osteochondrosis; SNP
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Corbin, L. J. (2013). Application of genomic technologies to the horse. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/11808
Chicago Manual of Style (16th Edition):
Corbin, Laura Jayne. “Application of genomic technologies to the horse.” 2013. Doctoral Dissertation, University of Edinburgh. Accessed December 11, 2019.
http://hdl.handle.net/1842/11808.
MLA Handbook (7th Edition):
Corbin, Laura Jayne. “Application of genomic technologies to the horse.” 2013. Web. 11 Dec 2019.
Vancouver:
Corbin LJ. Application of genomic technologies to the horse. [Internet] [Doctoral dissertation]. University of Edinburgh; 2013. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/1842/11808.
Council of Science Editors:
Corbin LJ. Application of genomic technologies to the horse. [Doctoral Dissertation]. University of Edinburgh; 2013. Available from: http://hdl.handle.net/1842/11808
Macquarie University
111.
Weng, Haijie.
Topics in financial risk management and fund management.
Degree: 2015, Macquarie University
URL: http://hdl.handle.net/1959.14/1067592
► Thesis by publication.
1. Abstract – 2. Introduction – 3. Style analysis and value-at-risk of Asia-focused hedge funds – 4. Agency theory and financial planning…
(more)
▼ Thesis by publication.
1. Abstract – 2. Introduction – 3. Style analysis and value-at-risk of Asia-focused hedge funds – 4. Agency theory and financial planning practice – 5. Backfilling financial data with an iterative PCA-based imputation – 6. Summary and conclusions – 7. References.
This PhD thesis analyses three key problems in financial risk management and fund management. First, it tackles the problems of identifying risk contributors and performing Value-at-Risk analysis for Asian hedge funds. Second, it sheds light on agency problems affecting funds management in Australia. Last but not least, it discusses the problems of cleansing financial historical data essential for risk management. The thesis consists of three key chapters based on two published journal articles and one research paper.
Chapter 3, titled Style Analysis and Value-at-Risk of Asia-focused Hedge Funds has been published in the Pacific-Basin Finance Journal, Volume 19 (2011). The chapter identifies risk factors and analyses Value-at-Risk (VaR) for Asia-focused hedge funds. Through a modified style analysis technique, we find that Asian hedge funds represented by Asian hedge fund indices show significant positive exposure to emerging equity markets. They also hold a significant portion of portfolio in cash and high credit rating bonds while taking short positions in world government and emerging market bonds. A rolling window style analysis is used to measure the time-varying risk exposure of Asian hedge funds. For both a static and rolling period style analysis, our model provides high explanatory power for returns on the considered hedge fund index. We further conduct a Value-at-Risk analysis using the results of a rolling window style analysis as inputs. Our results indicate that the accuracy of VaR models is dominated by their ability to capture the tail distribution of the hedge fund returns. Moreover, the distributional assumption seems to be more important than the chosen volatility model for the performance of the models in VaR prediction. Our findings further suggest that the considered parametric models outperform a simple historical simulation that is purely based on past return observations.
Chapter 4 is based on a journal article, titled Agency Theory and Financial Planning Practice that has been published in the Australian Economic Review, Volume 47 (2014). The chapter extends an influential contribution to the literature on agency theory and then uses this extension, along with other theoretical contributions, to shed light on agency problems affecting funds management and financial planning in Australia. The case for pure fee for service in actively managed funds and plans turns out to be weak. The amount of money exposed to risk by an active manager should be less than the entire investible wealth of the client, especially in the case of investors on the cusp of retirement. Asset-based fees on actively managed funds should include a fulcrum component.
Chapter 5 titled Backfilling Financial Data…
Advisors/Committee Members: Macquarie University. Department of Applied Finance and Actuarial Studies.
Subjects/Keywords: Financial risk management; Risk assessment; risk management; fund management; data imputation; hedge fund; agency theory
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APA ·
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APA (6th Edition):
Weng, H. (2015). Topics in financial risk management and fund management. (Doctoral Dissertation). Macquarie University. Retrieved from http://hdl.handle.net/1959.14/1067592
Chicago Manual of Style (16th Edition):
Weng, Haijie. “Topics in financial risk management and fund management.” 2015. Doctoral Dissertation, Macquarie University. Accessed December 11, 2019.
http://hdl.handle.net/1959.14/1067592.
MLA Handbook (7th Edition):
Weng, Haijie. “Topics in financial risk management and fund management.” 2015. Web. 11 Dec 2019.
Vancouver:
Weng H. Topics in financial risk management and fund management. [Internet] [Doctoral dissertation]. Macquarie University; 2015. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/1959.14/1067592.
Council of Science Editors:
Weng H. Topics in financial risk management and fund management. [Doctoral Dissertation]. Macquarie University; 2015. Available from: http://hdl.handle.net/1959.14/1067592
University of Cincinnati
112.
Deryol, Rustu.
Lifestyle, Self-Control, and School-Based Violent
Victimization in Turkey.
Degree: PhD, Education, Criminal Justice, and Human Services:
Criminal Justice, 2015, University of Cincinnati
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308217
► The present study aims to fill in the gap in the Turkish school-based violent victimization literature by exploring the predictors of general, crime-, and gender-specific…
(more)
▼ The present study aims to fill in the gap in the
Turkish school-based violent victimization literature by exploring
the predictors of general, crime-, and gender-specific violent
victimization. Research hypothesis are derived from the framework
of lifestyle-routine activities and self-control theories and are
tested using data from The National High School Offending and
Victimization Survey in Turkey. This survey was created and
conducted upon the initiative of Dr. Osman Dolu, teaching and
conducting research at the Police Academy in Ankara, Turkey. For
this dissertation, the data on school victimization is based on a
sub-sample from this national survey which included 1,204 students
from 15 Mersin high schools.Binary logistic regression models were
estimated using datasets with missing data and datasets with
missing values imputed. Results indicate that the propositions of
lifestyle-routine activities theory were generally supported.
Particularly, self-mutilation (as a measure of delinquent
lifestyle) was a robust predictor of general violent victimization
and crime-specific victimization. Similarly, friends’ delinquency,
measured with gang membership and the number of gang friends, also
impacted victimization in several models. Moreover, school-related
opportunity measures were also often significant. In particular,
school responsiveness to student misconduct, school control of
weapons, and unsupervised areas significantly estimated
school-based victimization in a number of models. However,
gender-specific analyses supported the notion of “gendered
opportunity” – that the predictors of opportunity for victimization
vary somewhat across males and females. Findings of this study also
support the results of much U.S.-based empirical literature about
the effect of low self-control on school-based violent
victimization in that it was a significant predictor of
victimization in many models, though its effect was mediated and/or
moderated in a number of instances. Also, its effect was somewhat
gendered. Low self-control exerted a significant effect on females,
and this effect was not mediated by opportunity measures across all
datasets. For males, it was significant only using a dataset with
imputed values, and this effect was mediated by opportunity
measures. Overall, an important methodological implication is that
future empirical studies that used survey data should employ
multiple
imputation methods in their analyses, as support for
theory is shown to be contingent on how missing cases are handled.
Implications of the findings for theory and practice within
multilevel school-based crime prevention framework along with the
limitations of the study are discussed in separate
sections.
Advisors/Committee Members: Wilcox, Pamela (Committee Chair).
Subjects/Keywords: Criminology; multiple imputation; school-based victimization; mediation; moderation; gender-specific opportunity; crime-specific opportunity
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Export
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Manager
APA (6th Edition):
Deryol, R. (2015). Lifestyle, Self-Control, and School-Based Violent
Victimization in Turkey. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308217
Chicago Manual of Style (16th Edition):
Deryol, Rustu. “Lifestyle, Self-Control, and School-Based Violent
Victimization in Turkey.” 2015. Doctoral Dissertation, University of Cincinnati. Accessed December 11, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308217.
MLA Handbook (7th Edition):
Deryol, Rustu. “Lifestyle, Self-Control, and School-Based Violent
Victimization in Turkey.” 2015. Web. 11 Dec 2019.
Vancouver:
Deryol R. Lifestyle, Self-Control, and School-Based Violent
Victimization in Turkey. [Internet] [Doctoral dissertation]. University of Cincinnati; 2015. [cited 2019 Dec 11].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308217.
Council of Science Editors:
Deryol R. Lifestyle, Self-Control, and School-Based Violent
Victimization in Turkey. [Doctoral Dissertation]. University of Cincinnati; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308217
113.
Audigier, Vincent.
Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes : Multiple imputation using principal component methods : A new methodology to deal with missing values.
Degree: Docteur es, Mathématiques appliquées, 2015, Rennes, Agrocampus Ouest
URL: http://www.theses.fr/2015NSARG015
► Cette thèse est centrée sur le développement de nouvelles méthodes d'imputation multiples, basées sur des techniques d'analyse factorielle. L'étude des méthodes factorielles, ici en tant…
(more)
▼ Cette thèse est centrée sur le développement de nouvelles méthodes d'imputation multiples, basées sur des techniques d'analyse factorielle. L'étude des méthodes factorielles, ici en tant que méthodes d'imputation, offre de grandes perspectives en termes de diversité du type de données imputées d'une part, et en termes de dimensions de jeux de données imputés d'autre part. Leur propriété de réduction de la dimension limite en effet le nombre de paramètres estimés.Dans un premier temps, une méthode d'imputation simple par analyse factorielle de données mixtes est détaillée. Ses propriétés sont étudiées, en particulier sa capacité à gérer la diversité des liaisons mises en jeu et à prendre en compte les modalités rares. Sa qualité de prédiction est éprouvée en la comparant à l'imputation par forêts aléatoires.Ensuite, une méthode d'imputation multiple pour des données quantitatives basée sur une approche Bayésienne du modèle d'analyse en composantes principales est proposée. Elle permet d'inférer en présence de données manquantes y compris quand le nombre d'individus est petit devant le nombre de variables, ou quand les corrélations entre variables sont fortes.Enfin, une méthode d'imputation multiple pour des données qualitatives par analyse des correspondances multiples (ACM) est proposée. La variabilité de prédiction des données manquantes est reflétée via un bootstrap non-paramétrique. L'imputation multiple par ACM offre une réponse au problème de l'explosion combinatoire limitant les méthodes concurrentes dès lors que le nombre de variables ou de modalités est élev
This thesis proposes new multiple imputation methods that are based on principal component methods, which were initially used for exploratory analysis and visualisation of continuous, categorical and mixed multidimensional data. The study of principal component methods for imputation, never previously attempted, offers the possibility to deal with many types and sizes of data. This is because the number of estimated parameters is limited due to dimensionality reduction.First, we describe a single imputation method based on factor analysis of mixed data. We study its properties and focus on its ability to handle complex relationships between variables, as well as infrequent categories. Its high prediction quality is highlighted with respect to the state-of-the-art single imputation method based on random forests.Next, a multiple imputation method for continuous data using principal component analysis (PCA) is presented. This is based on a Bayesian treatment of the PCA model. Unlike standard methods based on Gaussian models, it can still be used when the number of variables is larger than the number of individuals and when correlations between variables are strong.Finally, a multiple imputation method for categorical data using multiple correspondence analysis (MCA) is proposed. The variability of prediction of missing values is introduced via a non-parametric bootstrap approach. This helps to tackle the combinatorial issues which arise from the large…
Advisors/Committee Members: Husson, François (thesis director).
Subjects/Keywords: Données manquantes; Données mixtes; Données qualitatives; Imputation multiple; Imputation simple; Analyse factorielle des données mixtes; Analyse en composantes principales; Analyse des correspondances multiples; Bayésien; Bootstrap; Missing data; Mixed data; Categorical data; Multiple Imputation; Single Imputation; Factorial analysis of mixed data; Principal component analysis; Multiple correspondence analysis; Bayesian; Bootstrap
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APA ·
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APA (6th Edition):
Audigier, V. (2015). Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes : Multiple imputation using principal component methods : A new methodology to deal with missing values. (Doctoral Dissertation). Rennes, Agrocampus Ouest. Retrieved from http://www.theses.fr/2015NSARG015
Chicago Manual of Style (16th Edition):
Audigier, Vincent. “Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes : Multiple imputation using principal component methods : A new methodology to deal with missing values.” 2015. Doctoral Dissertation, Rennes, Agrocampus Ouest. Accessed December 11, 2019.
http://www.theses.fr/2015NSARG015.
MLA Handbook (7th Edition):
Audigier, Vincent. “Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes : Multiple imputation using principal component methods : A new methodology to deal with missing values.” 2015. Web. 11 Dec 2019.
Vancouver:
Audigier V. Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes : Multiple imputation using principal component methods : A new methodology to deal with missing values. [Internet] [Doctoral dissertation]. Rennes, Agrocampus Ouest; 2015. [cited 2019 Dec 11].
Available from: http://www.theses.fr/2015NSARG015.
Council of Science Editors:
Audigier V. Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes : Multiple imputation using principal component methods : A new methodology to deal with missing values. [Doctoral Dissertation]. Rennes, Agrocampus Ouest; 2015. Available from: http://www.theses.fr/2015NSARG015
Universidade Nova
114.
Sylvanus Udoette, Ubong.
Imputation techniques for improving survey outcomes in Nigeria: the case of the business expectation survey (BES) of the central bank of Nigeria.
Degree: 2017, Universidade Nova
URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/21449
► Over the years, the issue of respondents’ apathy, missing data and item non-response in particular, has remained a major concern with regards to analyses of…
(more)
▼ Over the years, the issue of respondents’ apathy, missing data and item non-response in
particular, has remained a major concern with regards to analyses of survey-based studies
undertaken by the Central Bank of Nigeria (CBN). Researchers and policy analysis within the
CBN has been plagued by the growing quantum of item non-response. This dissertation will
attempt to empirically analyze and recommend the best
imputation technique for item nonresponse
in surveys undertaken by the Bank. The case in point will be the Business
Expectations Survey (BES) conducted quarterly by the CBN. It will take a specific
items/questions in the BES for which there are complete responses and undertake a multiple
correspondence analysis (MCA) of the responses. Using a complete randomize scheme (table
of random numbers) it will exclude 15 – 35 percent of responses as if they were item nonresponse
and proceed to replace them through various
imputation technique. After which the
MCA will be repeated for each of the derived data sets and the result compared with that of
the original data sets. The matrices of principal coordinates are compared using the RV
coefficient (Escoufier, 1973), a measure of similarity between two datasets such that a value
of 1 indicates complete similarity and 0 indicates complete dissimilarity. This coefficient is a
generalization of the square of Spearman’s correlation coefficient. The result of the RV
coefficient analysis and well as the analysis of some selected summary statistics will be used
to recommend the best
imputation technique for such item non-responses in future surveys.
Advisors/Committee Members: Jorge Gomes, Paulo.
Subjects/Keywords: Missing data; Item Non-response; Imputation technique; RV coefficient; Multiple correspondence analysis
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APA (6th Edition):
Sylvanus Udoette, U. (2017). Imputation techniques for improving survey outcomes in Nigeria: the case of the business expectation survey (BES) of the central bank of Nigeria. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/21449
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sylvanus Udoette, Ubong. “Imputation techniques for improving survey outcomes in Nigeria: the case of the business expectation survey (BES) of the central bank of Nigeria.” 2017. Thesis, Universidade Nova. Accessed December 11, 2019.
http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/21449.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sylvanus Udoette, Ubong. “Imputation techniques for improving survey outcomes in Nigeria: the case of the business expectation survey (BES) of the central bank of Nigeria.” 2017. Web. 11 Dec 2019.
Vancouver:
Sylvanus Udoette U. Imputation techniques for improving survey outcomes in Nigeria: the case of the business expectation survey (BES) of the central bank of Nigeria. [Internet] [Thesis]. Universidade Nova; 2017. [cited 2019 Dec 11].
Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/21449.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sylvanus Udoette U. Imputation techniques for improving survey outcomes in Nigeria: the case of the business expectation survey (BES) of the central bank of Nigeria. [Thesis]. Universidade Nova; 2017. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/21449
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Iowa State University
115.
Sun, Yaxuan.
Statistical methods in modeling disease surveillance data with misclassification.
Degree: 2017, Iowa State University
URL: https://lib.dr.iastate.edu/etd/16223
► This thesis focuses on constructing appropriate statistical models to monitor the dynamics of disease transmission in animal disease surveillance system. One big challenge in analyzing…
(more)
▼ This thesis focuses on constructing appropriate statistical models to monitor the dynamics of disease transmission in animal disease surveillance system. One big challenge in analyzing such disease surveillance data is that the diagnostic tests are usually known to have imperfect sensitivity and specificity, thus the observations are usually misclassified, which introduces uncertainty in determination and modeling of the true disease status among animals. The thesis consists of three projects focusing on three different models and statistical inferences for different disease surveillance datasets. In the first project (Chapter 2), we propose a latent spatial piecewise exponential model for the misclassified disease surveillance data and apply the model to a data from the porcine reproductive and respiratory syndrome virus (PRRSV) disease. The misclassification of test outcomes are accounted for by using a two-level survival model. Spatial distance and time-varying covariates are incorporated to account for disease transmission. We show that our model is efficient in capturing the data features and easy to implement. In the second project (Chapter 3), we are motivated by parameter estimations in hidden Markov models (HMM) and mixed HMM (MHMM). The HMM can be applied to the animal disease surveillance data where the outcomes are with misclassification, and with a group level random effect added, the MHMM can model the correlation structure. However, the parameters estimation in these models are challenging because of the latent variables and random effect. We propose a pairwise fractional imputation using the idea of parametric fractional imputation as well as the Markov property. The proposed estimation method is shown to provide efficient parameter estimates and achieves computational efficiency. In the third project (Chapter 4),
we further investigate into the piecewise exponential model and consider estimation of the hazard functions where a monotone restriction is put on the hazard. When observations are with misclassification, the estimation involves EM-algorithm and the principle of isotonic regression is used for constraint optimization of the model parameters. Details of the estimation algorithm is developed in this chapter and the bootstrap confidence interval is constructed for measuring the variability of the estimates. The proposed model is then applied to another PRRSV surveillance study in the swine population.
Subjects/Keywords: Bayesian; Disease Surveillance; Fractional Imputation; Hidden Markov Model; Misclassification; Survival Analysis; Statistics and Probability
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sun, Y. (2017). Statistical methods in modeling disease surveillance data with misclassification. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16223
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sun, Yaxuan. “Statistical methods in modeling disease surveillance data with misclassification.” 2017. Thesis, Iowa State University. Accessed December 11, 2019.
https://lib.dr.iastate.edu/etd/16223.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sun, Yaxuan. “Statistical methods in modeling disease surveillance data with misclassification.” 2017. Web. 11 Dec 2019.
Vancouver:
Sun Y. Statistical methods in modeling disease surveillance data with misclassification. [Internet] [Thesis]. Iowa State University; 2017. [cited 2019 Dec 11].
Available from: https://lib.dr.iastate.edu/etd/16223.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sun Y. Statistical methods in modeling disease surveillance data with misclassification. [Thesis]. Iowa State University; 2017. Available from: https://lib.dr.iastate.edu/etd/16223
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Edinburgh
116.
Tsairidou, Smaragda.
Genetics of disease resistance : application to bovine tuberculosis.
Degree: PhD, 2016, University of Edinburgh
URL: http://hdl.handle.net/1842/25397
► Bovine Tuberculosis (bTB) is a disease of significant economic importance, being one of the most persistent animal health problems in the UK and the Republic…
(more)
▼ Bovine Tuberculosis (bTB) is a disease of significant economic importance, being one of the most persistent animal health problems in the UK and the Republic of Ireland and increasingly constituting a public health concern especially for the developing world. Limitations of the currently available diagnostic and control methods, along with our incomplete understanding of bTB transmission, prevent successful eradication. This Thesis addresses the development of a complementary control strategy which will be based on animal genetics and will allow us to identify animals genetically predisposed to be more resistant to disease. Specifically, the aim of my PhD project is to investigate the genetic architecture of resistance to bTB and demonstrate the feasibility of whole genome prediction for the control of bTB in cattle. Genomic selection for disease resistance in livestock populations will assist with the reduction of the in herd-level incidence and the severity of potential outbreaks. The first objective was to explore the estimation of breeding values for bTB resistance in UK dairy cattle, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates. Through using dense SNP chip data the results of Chapter 2 demonstrate that genomic selection for bTB resistance is feasible (h2 = 0.23(SE = 0.06)) and bTB resistance can be predicted using genetic markers with an estimate of prediction accuracy of r(g, ĝ) = 0.33 in this data. It was shown that genotypes help to predict disease state (AUC ≈ 0.58) and animals lacking bTB phenotypes can be selected based on their genotypes. In Chapter 3, a novel approach is presented to identify loci displaying heterozygote (dis)advantage associated with resistance to M. bovis, hypothesising underlying non-additive genetic variation, and these results are compared with those obtained from standard genome scans. A marker was identified suggesting an association between locus heterozygosity and increased susceptibility to bTB i.e. a heterozygote disadvantage, with the heterozygotes being significantly more in the cases than in the controls (x2 = 11.50, p < 0.001). Secondly, this thesis focused on conducting a meta-analysis on two dairy cattle populations with bTB phenotypes and SNP chip genotypes, identifying genomic regions underlying bTB resistance and testing genomic predictions by means of cross-validation. In Chapter 4, exploration of the genetic architecture of the trait revealed that bTB resistance is a moderately polygenic, complex trait with clusters of causal variants spread across a few major chromosomes collectively controlling the trait. A region was identified on chromosome 6, putatively associated with bTB resistance and this chromosome as a whole was shown to contribute a major proportion (hc 2= 0.051) of the observed variation in this dataset. Genomic prediction for bTB was shown to be feasible even when only distantly related populations are combined (r(g,ĝ)=0.33 (SE = 0.05)), with the chromosomal heritability results…
Subjects/Keywords: 636.2; bovine Tuberculosis; genomic selection; SICCT; Single Intradermal Comparative Cervical Test; prediction accuracy; imputation
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APA ·
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APA (6th Edition):
Tsairidou, S. (2016). Genetics of disease resistance : application to bovine tuberculosis. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/25397
Chicago Manual of Style (16th Edition):
Tsairidou, Smaragda. “Genetics of disease resistance : application to bovine tuberculosis.” 2016. Doctoral Dissertation, University of Edinburgh. Accessed December 11, 2019.
http://hdl.handle.net/1842/25397.
MLA Handbook (7th Edition):
Tsairidou, Smaragda. “Genetics of disease resistance : application to bovine tuberculosis.” 2016. Web. 11 Dec 2019.
Vancouver:
Tsairidou S. Genetics of disease resistance : application to bovine tuberculosis. [Internet] [Doctoral dissertation]. University of Edinburgh; 2016. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/1842/25397.
Council of Science Editors:
Tsairidou S. Genetics of disease resistance : application to bovine tuberculosis. [Doctoral Dissertation]. University of Edinburgh; 2016. Available from: http://hdl.handle.net/1842/25397
Uppsala University
117.
Huo, Zhao.
A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data.
Degree: Statistics, 2015, Uppsala University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256602
► Multiple imputation (MI) is a commonly used approach to impute missing data. This thesis studies missing covariates in recurrent event data, and discusses ways…
(more)
▼ Multiple imputation (MI) is a commonly used approach to impute missing data. This thesis studies missing covariates in recurrent event data, and discusses ways to include the survival outcomes in the imputation model. Some MI methods under consideration are the event indicator D combined with, respectively, the right-censored event times T, the logarithm of T and the cumulative baseline hazard H0(T). After imputation, we can then proceed to the complete data analysis. The Cox proportional hazards (PH) model and the PWP model are chosen as the analysis models, and the coefficient estimates are of substantive interest. A Monte Carlo simulation study is conducted to compare different MI methods, the relative bias and mean square error will be used in the evaluation process. Furthermore, an empirical study based on cardiovascular disease event data which contains missing values will be conducted. Overall, the results show that MI based on the Nelson-Aalen estimate of H0(T) is preferred in most circumstances.
Subjects/Keywords: Missing data; Multiple imputation; Missing covariates; Recurrent event data; Cox PH model; PWP model
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APA ·
Chicago ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Huo, Z. (2015). A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256602
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Huo, Zhao. “A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data.” 2015. Thesis, Uppsala University. Accessed December 11, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256602.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Huo, Zhao. “A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data.” 2015. Web. 11 Dec 2019.
Vancouver:
Huo Z. A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data. [Internet] [Thesis]. Uppsala University; 2015. [cited 2019 Dec 11].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256602.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Huo Z. A Comparsion of Multiple Imputation Methods for Missing Covariate Values in Recurrent Event Data. [Thesis]. Uppsala University; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256602
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Melbourne
118.
Apajee, Jemishabye.
An exploration of multiple imputation strategies for handling missing data in composite scores with incomplete items.
Degree: 2016, University of Melbourne
URL: http://hdl.handle.net/11343/112462
► Missing data are common in medical research. One area where missing data can arise is in composite scores (or scale scores) when one or more…
(more)
▼ Missing data are common in medical research. One area where missing data can arise is in composite scores (or scale scores) when one or more of the items that form the scale is incomplete. A method that is becoming increasingly popular for handling missing data is multiple imputation (MI). In the context of missing data in scale scores, MI can be applied at either the item level or the scale level. Various strategies have been proposed in the literature for imputing missing data at the scale level and the item level. Yet there is little comparison of these strategies in longitudinal settings and not much guidance is available about how to best implement these strategies. The challenge with using the available strategies in longitudinal studies is that one may want to impute missing data in several scales, each of which comprises a large number of items that have been measured at several waves, leading to large imputation models which may result in convergence problems. It is therefore important to evaluate the performance of these strategies in longitudinal settings to provide proper guidance for users of MI.
In this thesis, I used a simulation study and a real example from the Longitudinal Study of Australian Children (LSAC) to compare the performance of the four MI strategies that are available for handling missing data in composite scores within a longitudinal setting. These strategies are: scale-level imputation using scale scores as auxiliary variables; the “standard” item-level imputation, which uses other items as auxiliary variables; item-level imputation using scale scores as auxiliary variables; and item-level imputation using principal components, derived from other items, as auxiliary variables. I also compared the effect of implementing these strategies using two MI approaches, multivariate normal imputation (MVNI) and fully conditional specification (FCS).
While the literature recommends item-level imputation over scale level imputation, the research in this thesis demonstrates that when implemented using FCS, item-level imputation, with items from other scales as auxiliary variables, could produce biased parameter estimates. This research also provides support for using scales scores or principal components as auxiliary variables in item-level imputation models when the “standard” item-level imputation strategy cannot be used due to convergence problems.
Subjects/Keywords: multiple imputation; missing data; principle component analysis; composite scores; scale scores; items
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Apajee, J. (2016). An exploration of multiple imputation strategies for handling missing data in composite scores with incomplete items. (Masters Thesis). University of Melbourne. Retrieved from http://hdl.handle.net/11343/112462
Chicago Manual of Style (16th Edition):
Apajee, Jemishabye. “An exploration of multiple imputation strategies for handling missing data in composite scores with incomplete items.” 2016. Masters Thesis, University of Melbourne. Accessed December 11, 2019.
http://hdl.handle.net/11343/112462.
MLA Handbook (7th Edition):
Apajee, Jemishabye. “An exploration of multiple imputation strategies for handling missing data in composite scores with incomplete items.” 2016. Web. 11 Dec 2019.
Vancouver:
Apajee J. An exploration of multiple imputation strategies for handling missing data in composite scores with incomplete items. [Internet] [Masters thesis]. University of Melbourne; 2016. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/11343/112462.
Council of Science Editors:
Apajee J. An exploration of multiple imputation strategies for handling missing data in composite scores with incomplete items. [Masters Thesis]. University of Melbourne; 2016. Available from: http://hdl.handle.net/11343/112462
McMaster University
119.
Cui, Qu.
EFFECT OF SMOKING AND CESSATION IN HIV-INFECTED PEOPLE.
Degree: PhD, 2011, McMaster University
URL: http://hdl.handle.net/11375/11167
► Cigarette smoking is prevalent in HIV-infected people, resulting in higher mortality rate and more premature heart and lung diseases in the highly active antiretroviral…
(more)
▼ Cigarette smoking is prevalent in HIV-infected people, resulting in higher mortality rate and more premature heart and lung diseases in the highly active antiretroviral therapy era. Smoking is a modifiable risk factor for these adverse outcomes and smoking cessation in HIV-positive smokers is feasible, although further efforts are needed to improve smoking cessation programs in HIV-positive persons. In this thesis, I examined the role of smoking in mortality and morbidity in HIV-positive Ontarians, and piloted a smoking cessation program featuring a novel smoking cessation aid, varenicline, in HIV-infected smokers. In addition, I explored three different methods to resolve missing data, by excluding, grouping and multiply imputing missing data. I adopted three different study designs in my thesis studies: retrospective cohort, cross-sectional and open label study. We found smoking prevalence in HIV-infected people was consistently higher than in the general population. Smoking was associated with a higher risk of death, of respiratory symptoms, hospitalization and chronic obstructive pulmonary disease, and with reduced lung function and less CD4-T-lymphocyte improvement over time. We found varenicline was as effective in HIV-positive smokers as in non-HIV smokers reported by previous studies.
Doctor of Philosophy (PhD)
Advisors/Committee Members: Smieja, Marek Jozef, Thabane, Lehana, Andrew McIvor, Fiona Smaill, Health Research Methodology.
Subjects/Keywords: HIV; smoking; smoking cessation; multiple imputation; varenicline; Canada; Clinical Epidemiology; Clinical Epidemiology
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APA ·
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MLA ·
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Export
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APA (6th Edition):
Cui, Q. (2011). EFFECT OF SMOKING AND CESSATION IN HIV-INFECTED PEOPLE. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/11167
Chicago Manual of Style (16th Edition):
Cui, Qu. “EFFECT OF SMOKING AND CESSATION IN HIV-INFECTED PEOPLE.” 2011. Doctoral Dissertation, McMaster University. Accessed December 11, 2019.
http://hdl.handle.net/11375/11167.
MLA Handbook (7th Edition):
Cui, Qu. “EFFECT OF SMOKING AND CESSATION IN HIV-INFECTED PEOPLE.” 2011. Web. 11 Dec 2019.
Vancouver:
Cui Q. EFFECT OF SMOKING AND CESSATION IN HIV-INFECTED PEOPLE. [Internet] [Doctoral dissertation]. McMaster University; 2011. [cited 2019 Dec 11].
Available from: http://hdl.handle.net/11375/11167.
Council of Science Editors:
Cui Q. EFFECT OF SMOKING AND CESSATION IN HIV-INFECTED PEOPLE. [Doctoral Dissertation]. McMaster University; 2011. Available from: http://hdl.handle.net/11375/11167
120.
Chen, Senniang.
Imputation of missing values using quantile regression.
Degree: 2014, Iowa State University
URL: https://lib.dr.iastate.edu/etd/13924
► In this thesis, we consider an imputation method to handle missing response values based on quantile regression estimation. In the proposed method, the missing response…
(more)
▼ In this thesis, we consider an imputation method to handle missing response values based on quantile regression estimation. In the proposed method, the missing response values are generated using the estimated conditional quantile regression function at given values of covariates parametrically or semiparametrically. We adopt the generalized method of moments and the empirical likelihood method for estimation of parameters defined through a general estimating equation. We demonstrate that the proposed estimators, which combine both quantile regression imputation (parametric or semiparametric) and general estimating equation methods
(generalized method of moments or empirical likelihood), have competitive advantages over some of the most widely used parametric and non-parametric imputation estimators. The consistency and the asymptotic normality of our estimators are established and variance estimation is provided. Results from a limited simulation study and an empirical study are presented to
show the adequacy of the proposed methods.
Subjects/Keywords: Empirical likelihood; Generalized method of moments; Imputation; Quantile regression; Statistics and Probability
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chen, S. (2014). Imputation of missing values using quantile regression. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/13924
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Chen, Senniang. “Imputation of missing values using quantile regression.” 2014. Thesis, Iowa State University. Accessed December 11, 2019.
https://lib.dr.iastate.edu/etd/13924.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Chen, Senniang. “Imputation of missing values using quantile regression.” 2014. Web. 11 Dec 2019.
Vancouver:
Chen S. Imputation of missing values using quantile regression. [Internet] [Thesis]. Iowa State University; 2014. [cited 2019 Dec 11].
Available from: https://lib.dr.iastate.edu/etd/13924.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Chen S. Imputation of missing values using quantile regression. [Thesis]. Iowa State University; 2014. Available from: https://lib.dr.iastate.edu/etd/13924
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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Open Access Theses and Dissertations
