subject:( Imputation multiple deux niveaux)

Université de Sherbrooke

1. Bernard, Francis. Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes .

Degree: 2013, Université de Sherbrooke

URL: http://hdl.handle.net/11143/6571

► Lorsqu'on réalise une analyse des données dans le cadre d'une enquête, on est souvent confronté au problème des données manquantes. L'une des solutions les plus… (more)

Subjects/Keywords: Valeur manquante; Incertitude; Méthodes basées sur le rééchantillonnage; Imputation multiple à deux niveaux; Imputation multiple conventionnelle

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APA (6^th Edition):

Bernard, F. (2013). Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes . (Masters Thesis). Université de Sherbrooke. Retrieved from http://hdl.handle.net/11143/6571

Chicago Manual of Style (16^th Edition):

Bernard, Francis. “Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes .” 2013. Masters Thesis, Université de Sherbrooke. Accessed December 08, 2019. http://hdl.handle.net/11143/6571.

MLA Handbook (7^th Edition):

Bernard, Francis. “Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes .” 2013. Web. 08 Dec 2019.

Vancouver:

Bernard F. Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes . [Internet] [Masters thesis]. Université de Sherbrooke; 2013. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/11143/6571.

Council of Science Editors:

Bernard F. Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes . [Masters Thesis]. Université de Sherbrooke; 2013. Available from: http://hdl.handle.net/11143/6571

University of Georgia

2. Wang, Qun. Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables.

Degree: MS, Statistics, 2004, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms

► The presence of missing or incomplete data is a ubiquitous problem in real world datasets. In the thesis, we apply multiple imputation procedures to analyze… (more)

Subjects/Keywords: multiple imputation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, Q. (2004). Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms

Chicago Manual of Style (16^th Edition):

Wang, Qun. “Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables.” 2004. Masters Thesis, University of Georgia. Accessed December 08, 2019. http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms.

MLA Handbook (7^th Edition):

Wang, Qun. “Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables.” 2004. Web. 08 Dec 2019.

Vancouver:

Wang Q. Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables. [Internet] [Masters thesis]. University of Georgia; 2004. [cited 2019 Dec 08]. Available from: http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms.

Council of Science Editors:

Wang Q. Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables. [Masters Thesis]. University of Georgia; 2004. Available from: http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms

Victoria University of Wellington

3. Luo, Maoxin. Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data.

Degree: 2013, Victoria University of Wellington

URL: http://hdl.handle.net/10063/2796

► The Food Nutrition Environment Survey (FNES) is a survey of New Zealand early childhood centres and schools and the food and nutritional services that they… (more)

Subjects/Keywords: Multiple imputation; Bayesian; Missing data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Luo, M. (2013). Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2796

Chicago Manual of Style (16^th Edition):

Luo, Maoxin. “Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data.” 2013. Masters Thesis, Victoria University of Wellington. Accessed December 08, 2019. http://hdl.handle.net/10063/2796.

MLA Handbook (7^th Edition):

Luo, Maoxin. “Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data.” 2013. Web. 08 Dec 2019.

Vancouver:

Luo M. Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data. [Internet] [Masters thesis]. Victoria University of Wellington; 2013. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10063/2796.

Council of Science Editors:

Luo M. Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data. [Masters Thesis]. Victoria University of Wellington; 2013. Available from: http://hdl.handle.net/10063/2796

University of the Western Cape

4. Karangwa, Innocent. Imputation techniques for non-ordered categorical missing data .

Degree: 2016, University of the Western Cape

URL: http://hdl.handle.net/11394/5061

► Missing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of… (more)

▼ Missing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of missing data may lead to bias in the estimates and incorrect inferences. Therefore, special attention is needed when analysing incomplete data. The multivariate normal imputation (MVNI) and the multiple imputation by chained equations (MICE) have emerged as the best techniques to impute or fills in missing data. The former assumes a normal distribution of the variables in the imputation model, but can also handle missing data whose distributions are not normal. The latter fills in missing values taking into account the distributional form of the variables to be imputed. The aim of this study was to determine the performance of these methods when data are missing at random (MAR) or completely at random (MCAR) on unordered or nominal categorical variables treated as predictors or response variables in the regression models. Both dichotomous and polytomous variables were considered in the analysis. The baseline data used was the 2007 Demographic and Health Survey (DHS) from the Democratic Republic of Congo. The analysis model of interest was the logistic regression model of the woman’s contraceptive method use status on her marital status, controlling or not for other covariates (continuous, nominal and ordinal). Based on the data set with missing values, data sets with missing at random and missing completely at random observations on either the covariates or response variables measured on nominal scale were first simulated, and then used for imputation purposes. Under MVNI method, unordered categorical variables were first dichotomised, and then K − 1 (where K is the number of levels of the categorical variable of interest) dichotomised variables were included in the imputation model, leaving the other category as a reference. These variables were imputed as continuous variables using a linear regression model. Imputation with MICE considered the distributional form of each variable to be imputed. That is, imputations were drawn using binary and multinomial logistic regressions for dichotomous and polytomous variables respectively. The performance of these methods was evaluated in terms of bias and standard errors in regression coefficients that were estimated to determine the association between the woman’s contraceptive methods use status and her marital status, controlling or not for other types of variables. The analysis was done assuming that the sample was not weighted fi then the sample weight was taken into account to assess whether the sample design would affect the performance of the multiple imputation methods of interest, namely MVNI and MICE. As expected, the results showed that for all the models, MVNI and MICE produced less biased smaller standard errors than the case deletion (CD) method, which discards items with missing values from the analysis. Moreover, it was found that when data were missing (MCAR or MAR) on the nominal variables that… Advisors/Committee Members: Kotze, Danelle (advisor), Blignaut, Renette (advisor).

Subjects/Keywords: Missing data; Multiple imputation; Multiple imputation by chained equations; Multivariate normal imputation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Karangwa, I. (2016). Imputation techniques for non-ordered categorical missing data . (Thesis). University of the Western Cape. Retrieved from http://hdl.handle.net/11394/5061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Karangwa, Innocent. “Imputation techniques for non-ordered categorical missing data .” 2016. Thesis, University of the Western Cape. Accessed December 08, 2019. http://hdl.handle.net/11394/5061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Karangwa, Innocent. “Imputation techniques for non-ordered categorical missing data .” 2016. Web. 08 Dec 2019.

Vancouver:

Karangwa I. Imputation techniques for non-ordered categorical missing data . [Internet] [Thesis]. University of the Western Cape; 2016. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/11394/5061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Karangwa I. Imputation techniques for non-ordered categorical missing data . [Thesis]. University of the Western Cape; 2016. Available from: http://hdl.handle.net/11394/5061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of the Western Cape

5. Brydon, Humphrey Charles. Missing imputation methods explored in big data analytics .

Degree: 2018, University of the Western Cape

URL: http://hdl.handle.net/11394/6605

► The aim of this study is to look at the methods and processes involved in imputing missing data and more specifically, complete missing blocks of… (more)

▼ The aim of this study is to look at the methods and processes involved in imputing missing data and more specifically, complete missing blocks of data. A further aim of this study is to look at the effect that the imputed data has on the accuracy of various predictive models constructed on the imputed data and hence determine if the imputation method involved is suitable. The identification of the missingness mechanism present in the data should be the first process to follow in order to identify a possible imputation method. The identification of a suitable imputation method is easier if the mechanism can be identified as one of the following; missing completely at random (MCAR), missing at random (MAR) or not missing at random (NMAR). Predictive models constructed on the complete imputed data sets are shown to be less accurate for those models constructed on data sets which employed a hot-deck imputation method. The data sets which employed either a single or multiple Monte Carlo Markov Chain (MCMC) or the Fully Conditional Specification (FCS) imputation methods are shown to result in predictive models that are more accurate. The addition of an iterative bagging technique in the modelling procedure is shown to produce highly accurate prediction estimates. The bagging technique is applied to variants of the neural network, a decision tree and a multiple linear regression (MLR) modelling procedure. A stochastic gradient boosted decision tree (SGBT) is also constructed as a comparison to the bagged decision tree. Final models are constructed from 200 iterations of the various modelling procedures using a 60% sampling ratio in the bagging procedure. It is further shown that the addition of the bagging technique in the MLR modelling procedure can produce a MLR model that is more accurate than that of the other more advanced modelling procedures under certain conditions. The evaluation of the predictive models constructed on imputed data is shown to vary based on the type of fit statistic used. It is shown that the average squared error reports little difference in the accuracy levels when compared to the results of the Mean Absolute Prediction Error (MAPE). The MAPE fit statistic is able to magnify the difference in the prediction errors reported. The Normalized Mean Bias Error (NMBE) results show that all predictive models constructed produced estimates that were an over-prediction, although these did vary depending on the data set and modelling procedure used. The Nash Sutcliffe efficiency (NSE) was used as a comparison statistic to compare the accuracy of the predictive models in the context of imputed data. The NSE statistic showed that the estimates of the models constructed on the imputed data sets employing a multiple imputation method were highly accurate. The NSE statistic results reported that the estimates from the predictive models constructed on the hot-deck imputed data were inaccurate and that a mean substitution of the fully observed data would have been a better method of imputation. The conclusion… Advisors/Committee Members: Blignaut, Renette (advisor).

Subjects/Keywords: Missing data; Imputation methods; Multiple imputation; Neural network; Bagging

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Brydon, H. C. (2018). Missing imputation methods explored in big data analytics . (Thesis). University of the Western Cape. Retrieved from http://hdl.handle.net/11394/6605

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Brydon, Humphrey Charles. “Missing imputation methods explored in big data analytics .” 2018. Thesis, University of the Western Cape. Accessed December 08, 2019. http://hdl.handle.net/11394/6605.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Brydon, Humphrey Charles. “Missing imputation methods explored in big data analytics .” 2018. Web. 08 Dec 2019.

Vancouver:

Brydon HC. Missing imputation methods explored in big data analytics . [Internet] [Thesis]. University of the Western Cape; 2018. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/11394/6605.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Brydon HC. Missing imputation methods explored in big data analytics . [Thesis]. University of the Western Cape; 2018. Available from: http://hdl.handle.net/11394/6605

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

6. Amer, Safaa R. Neural network imputation : a new fashion or a good tool.

Degree: PhD, Statistics, 2004, Oregon State University

URL: http://hdl.handle.net/1957/29926

► Most statistical surveys and data collection studies encounter missing data. A common solution to this problem is to discard observations with missing data while reporting… (more)

Subjects/Keywords: Multiple imputation (Statistics)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Amer, S. R. (2004). Neural network imputation : a new fashion or a good tool. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/29926

Chicago Manual of Style (16^th Edition):

Amer, Safaa R. “Neural network imputation : a new fashion or a good tool.” 2004. Doctoral Dissertation, Oregon State University. Accessed December 08, 2019. http://hdl.handle.net/1957/29926.

MLA Handbook (7^th Edition):

Amer, Safaa R. “Neural network imputation : a new fashion or a good tool.” 2004. Web. 08 Dec 2019.

Vancouver:

Amer SR. Neural network imputation : a new fashion or a good tool. [Internet] [Doctoral dissertation]. Oregon State University; 2004. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1957/29926.

Council of Science Editors:

Amer SR. Neural network imputation : a new fashion or a good tool. [Doctoral Dissertation]. Oregon State University; 2004. Available from: http://hdl.handle.net/1957/29926

University of Oxford

7. Rombach, Ines. The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials.

Degree: PhD, 2016, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437

► Missing data is a potential source of bias in the results of randomised controlled trials (RCTs), which can have a negative impact on guidance derived… (more)

▼ Missing data is a potential source of bias in the results of randomised controlled trials (RCTs), which can have a negative impact on guidance derived from them, and ultimately patient care. This thesis aims to improve the understanding, handling, analysis and reporting of missing data in patient reported outcome measures (PROMs) for RCTs. A review of the literature provided evidence of discrepancies between recommended methodology and current practice in the handling and reporting of missing data. Particularly, missed opportunities to minimise missing data, the use of inappropriate analytical methods and lack of sensitivity analyses were noted. Missing data patterns were examined and found to vary between PROMs as well as across RCTs. Separate analyses illustrated difficulties in predicting missing data, resulting in uncertainty about assumed underlying missing data mechanisms. Simulation work was used to assess the comparative performance of statistical approaches for handling missing available in standard statistical software. Multiple imputation (MI) at either the item, subscale or composite score level was considered for missing PROMs data at a single follow-up time point. The choice of an MI approach depended on a multitude of factors, with MI at the item level being more beneficial than its alternatives for high proportions of item missingness. The approaches performed similarly for high proportions of unit-nonresponse; however, convergence issues were observed for MI at the item level. Maximum likelihood (ML), MI and inverse probability weighting (IPW) were evaluated for handling missing longitudinal PROMs data. MI was less biased than ML when additional post-randomisation data were available, while IPW introduced more bias compared to both ML and MI. A case study was used to explore approaches to sensitivity analyses to assess the impact of missing data. It was found that trial results could be susceptible to varying assumptions about missing data, and the importance of interpreting the results in this context was reiterated. This thesis provides researchers with guidance for the handling and reporting of missing PROMs data in order to decrease bias arising from missing data in RCTs.

Subjects/Keywords: Multiple imputation; Missing data; Randomised controlled trials

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APA (6^th Edition):

Rombach, I. (2016). The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437

Chicago Manual of Style (16^th Edition):

Rombach, Ines. “The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials.” 2016. Doctoral Dissertation, University of Oxford. Accessed December 08, 2019. http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437.

MLA Handbook (7^th Edition):

Rombach, Ines. “The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials.” 2016. Web. 08 Dec 2019.

Vancouver:

Rombach I. The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials. [Internet] [Doctoral dissertation]. University of Oxford; 2016. [cited 2019 Dec 08]. Available from: http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437.

Council of Science Editors:

Rombach I. The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials. [Doctoral Dissertation]. University of Oxford; 2016. Available from: http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437

Wayne State University

8. Grace, Tammy A. The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test.

Degree: PhD, Education Evaluation and Research, 2016, Wayne State University

URL: https://digitalcommons.wayne.edu/oa_dissertations/1536

► ABSTRACT THE IMPACT OF MULTIPLE IMPUTATION ON THE TYPE II ERROR RATE OF THE T TEST by TAMMY A. GRACE August 2016 Advisor: Shlomo… (more)

Subjects/Keywords: Multiple Imputation; T Test; Statistics and Probability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Grace, T. A. (2016). The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/1536

Chicago Manual of Style (16^th Edition):

Grace, Tammy A. “The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test.” 2016. Doctoral Dissertation, Wayne State University. Accessed December 08, 2019. https://digitalcommons.wayne.edu/oa_dissertations/1536.

MLA Handbook (7^th Edition):

Grace, Tammy A. “The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test.” 2016. Web. 08 Dec 2019.

Vancouver:

Grace TA. The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test. [Internet] [Doctoral dissertation]. Wayne State University; 2016. [cited 2019 Dec 08]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1536.

Council of Science Editors:

Grace TA. The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test. [Doctoral Dissertation]. Wayne State University; 2016. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1536

Rice University

9. Berliner, Nathan K. Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness.

Degree: MA, Engineering, 2015, Rice University

URL: http://hdl.handle.net/1911/87747

► In this thesis multiple imputation, survival analysis, and propensity score analysis are combined in order to answer questions about treatment efficacy in cancer data with… (more)

Subjects/Keywords: Multiple Imputation; Survival Analysis; Causal Analysis

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APA (6^th Edition):

Berliner, N. K. (2015). Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/87747

Chicago Manual of Style (16^th Edition):

Berliner, Nathan K. “Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness.” 2015. Masters Thesis, Rice University. Accessed December 08, 2019. http://hdl.handle.net/1911/87747.

MLA Handbook (7^th Edition):

Berliner, Nathan K. “Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness.” 2015. Web. 08 Dec 2019.

Vancouver:

Berliner NK. Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness. [Internet] [Masters thesis]. Rice University; 2015. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1911/87747.

Council of Science Editors:

Berliner NK. Using Multiple Imputation, Survival Analysis, And Propensity Score Analysis In Cancer Data With Missingness. [Masters Thesis]. Rice University; 2015. Available from: http://hdl.handle.net/1911/87747

University of Notre Dame

10. Justin M. Luningham. Evaluating Psychometric and Imputation-Based Methods for Data Integration</h1>.

Degree: PhD, Psychology, 2018, University of Notre Dame

URL: https://curate.nd.edu/show/xd07gq70t5b

► IDA refers to combining data from independent studies into a concatenated dataset and analyzing the new data jointly (Curran & Hussong, 2009). IDA is… (more)

▼ IDA refers to combining data from independent studies into a concatenated dataset and analyzing the new data jointly (Curran & Hussong, 2009). IDA is an alternative to meta-analysis, which synthesizes summary statistics (such as effect sizes or standardized parameter estimates) from multiple studies. Pooling data for research is not a new idea; however, it has seen limited use in psychology. The IDA framework is innovative in its use of different models and methods to account for heterogeneity across different data sources. This dissertation evaluates the performance of particular applications of IDA in the field of behavior genetics, where meta-analysis is common practice. Behavior genetics researchers often perform exhaustive searches across the entire genome seeking genetic markers that are associated with an observed outcome. Because there are millions of genetic markers, and because even true gene associations have extremely tiny effect sizes, genome-wide searches are often combined across multiple studies to increase statistical power. Regression coefficients from tests of association are meta-analyzed, even though psychological or behavioral outcomes may be defined differently across studies. This dissertation tests the hypothesis that more precise measurement of an integrated outcome, achieved through IDA, can provide added power over typical meta-analysis in multi-study genome-wide searches. In chapter 1, a general overview of the IDA framework is discussed, genome-wide association studies are presented, and IDA for genome-wide searches is presented. Chapter 2 covers a simulation study evaluating measurement model IDA using a proposed bi-factor integration model. Chapter 3 covers simulation studies investigating multiple imputation IDA performance with a novel imputation model, namely, boosted decision trees as an extension of single-tree imputation. Chapter 4 presents an application of IDA to the Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies (ACTION) Consortium, a large-scale collaboration of six research projects studying the genetic underpinnings of aggression in children. Advisors/Committee Members: Gitta Lubke, Research Director.

Subjects/Keywords: Integrative Data Analysis; Multiple Imputation; Genetic Consortia

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APA (6^th Edition):

Luningham, J. M. (2018). Evaluating Psychometric and Imputation-Based Methods for Data Integration</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/xd07gq70t5b

Chicago Manual of Style (16^th Edition):

Luningham, Justin M.. “Evaluating Psychometric and Imputation-Based Methods for Data Integration</h1>.” 2018. Doctoral Dissertation, University of Notre Dame. Accessed December 08, 2019. https://curate.nd.edu/show/xd07gq70t5b.

MLA Handbook (7^th Edition):

Luningham, Justin M.. “Evaluating Psychometric and Imputation-Based Methods for Data Integration</h1>.” 2018. Web. 08 Dec 2019.

Vancouver:

Luningham JM. Evaluating Psychometric and Imputation-Based Methods for Data Integration</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2018. [cited 2019 Dec 08]. Available from: https://curate.nd.edu/show/xd07gq70t5b.

Council of Science Editors:

Luningham JM. Evaluating Psychometric and Imputation-Based Methods for Data Integration</h1>. [Doctoral Dissertation]. University of Notre Dame; 2018. Available from: https://curate.nd.edu/show/xd07gq70t5b

11. Magalhães, Ismenia Blavatsky de. Avaliação de redes Bayesianas para imputação em variáveis qualitativas e quantitativas.

Degree: PhD, Engenharia Mecânica, 2007, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/3/3132/tde-06072007-145922/ ;

►

Redes Bayesianas são estruturas que combinam distribuições de probabilidade e grafos. Apesar das redes Bayesianas terem surgido na década de 80 e as primeiras tentativas… (more)

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Redes Bayesianas são estruturas que combinam distribuições de probabilidade e grafos. Apesar das redes Bayesianas terem surgido na década de 80 e as primeiras tentativas em solucionar os problemas gerados a partir da não resposta datarem das décadas de 30 e 40, a utilização de estruturas deste tipo especificamente para imputação é bem recente: em 2002 em institutos oficiais de estatística e em 2003 no contexto de mineração de dados. O intuito deste trabalho é o de fornecer alguns resultados da aplicação de redes Bayesianas discretas e mistas para imputação. Para isso é proposto um algoritmo que combina o conhecimento de especialistas e dados experimentais observados de pesquisas anteriores ou parte dos dados coletados. Ao empregar as redes Bayesianas neste contexto, parte-se da hipótese de que uma vez preservadas as variáveis em sua relação original, o método de imputação será eficiente em manter propriedades desejáveis. Neste sentido, foram avaliados três tipos de consistências já existentes na literatura: a consistência da base de dados, a consistência lógica e a consistência estatística, e propôs-se a consistência estrutural, que se define como sendo a capacidade de a rede manter sua estrutura na classe de equivalência da rede original quando construída a partir dos dados após a imputação. É utilizada pela primeira vez uma rede Bayesiana mista para o tratamento da não resposta em variáveis quantitativas. Calcula-se uma medida de consistência estatística para redes mistas usando como recurso a imputação múltipla para a avaliação de parâmetros da rede e de modelos de regressão. Como aplicação foram conduzidos experimentos com base nos dados de domicílios e pessoas do Censo Demográfico 2000 do município de Natal e nos dados de um estudo sobre homicídios em Campinas. Dos resultados afirma-se que as redes Bayesianas para imputação em atributos discretos são promissoras, principalmente se o interesse estiver em manter a consistência estatística e o número de classes da variável for pequeno. Já para outras características, como o coeficiente de contingência entre as variáveis, são afetadas pelo método à medida que se aumenta o percentual de não resposta. Nos atributos contínuos, a mediana apresenta-se mais sensível ao método.

Bayesian networks are structures that combine probability distributions with graphs. Although Bayesian networks initially appeared in the 1980s and the first attempts to solve the problems generated from the non-response date back to the 1930s and 1940s, the use of structures of this kind specifically for imputation is rather recent: in 2002 by official statistical institutes, and in 2003 in the context of data mining. The purpose of this work is to present some results on the application of discrete and mixed Bayesian networks for imputation. For that purpose, we present an algorithm combining knowledge obtained from experts with experimental data derived from previous research or part of the collected data. To apply Bayesian networks in this context, it is assumed that once the variables are…

Advisors/Committee Members: Cozman, Fabio Gagliardi.

Subjects/Keywords: Bayesian networks; Imputação; Imputação múltipla; Imputation; Missing data; Multiple imputation; Não resposta; Redes Bayesianas

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Magalhães, I. B. d. (2007). Avaliação de redes Bayesianas para imputação em variáveis qualitativas e quantitativas. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/3/3132/tde-06072007-145922/ ;

Chicago Manual of Style (16^th Edition):

Magalhães, Ismenia Blavatsky de. “Avaliação de redes Bayesianas para imputação em variáveis qualitativas e quantitativas.” 2007. Doctoral Dissertation, University of São Paulo. Accessed December 08, 2019. http://www.teses.usp.br/teses/disponiveis/3/3132/tde-06072007-145922/ ;.

MLA Handbook (7^th Edition):

Magalhães, Ismenia Blavatsky de. “Avaliação de redes Bayesianas para imputação em variáveis qualitativas e quantitativas.” 2007. Web. 08 Dec 2019.

Vancouver:

Magalhães IBd. Avaliação de redes Bayesianas para imputação em variáveis qualitativas e quantitativas. [Internet] [Doctoral dissertation]. University of São Paulo; 2007. [cited 2019 Dec 08]. Available from: http://www.teses.usp.br/teses/disponiveis/3/3132/tde-06072007-145922/ ;.

Council of Science Editors:

Magalhães IBd. Avaliação de redes Bayesianas para imputação em variáveis qualitativas e quantitativas. [Doctoral Dissertation]. University of São Paulo; 2007. Available from: http://www.teses.usp.br/teses/disponiveis/3/3132/tde-06072007-145922/ ;

East Carolina University

12. Siver, Sydney R. Methods for Handling Missing Data for Multiple-Item Questionnaires.

Degree: 2017, East Carolina University

URL: http://hdl.handle.net/10342/6517

► Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human… (more)

Subjects/Keywords: person mean imputation; Monte Carlo; Missing observations (Statistics); Multiple imputation (Statistics); Questionnaires

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APA (6^th Edition):

Siver, S. R. (2017). Methods for Handling Missing Data for Multiple-Item Questionnaires. (Thesis). East Carolina University. Retrieved from http://hdl.handle.net/10342/6517

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Siver, Sydney R. “Methods for Handling Missing Data for Multiple-Item Questionnaires.” 2017. Thesis, East Carolina University. Accessed December 08, 2019. http://hdl.handle.net/10342/6517.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Siver, Sydney R. “Methods for Handling Missing Data for Multiple-Item Questionnaires.” 2017. Web. 08 Dec 2019.

Vancouver:

Siver SR. Methods for Handling Missing Data for Multiple-Item Questionnaires. [Internet] [Thesis]. East Carolina University; 2017. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10342/6517.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Siver SR. Methods for Handling Missing Data for Multiple-Item Questionnaires. [Thesis]. East Carolina University; 2017. Available from: http://hdl.handle.net/10342/6517

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

George Mason University

13. Cheema, Jehanzeb. Handling Missing Data in Educational Research Using SPSS .

Degree: 2012, George Mason University

URL: http://hdl.handle.net/1920/7840

► This study looked at the effect of a number of factors such as the choice of analytical method, the handling method for missing data, sample… (more)

Subjects/Keywords: Imputation Method; Item Non-Response; Listwise Deletion; Missing Data; Multiple Imputation; Missing Value

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cheema, J. (2012). Handling Missing Data in Educational Research Using SPSS . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/7840

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cheema, Jehanzeb. “Handling Missing Data in Educational Research Using SPSS .” 2012. Thesis, George Mason University. Accessed December 08, 2019. http://hdl.handle.net/1920/7840.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cheema, Jehanzeb. “Handling Missing Data in Educational Research Using SPSS .” 2012. Web. 08 Dec 2019.

Vancouver:

Cheema J. Handling Missing Data in Educational Research Using SPSS . [Internet] [Thesis]. George Mason University; 2012. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1920/7840.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cheema J. Handling Missing Data in Educational Research Using SPSS . [Thesis]. George Mason University; 2012. Available from: http://hdl.handle.net/1920/7840

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Maillet, Olivier. Processus stochastiques et non-linéaires dans les systèmes nano-électro-mécaniques : Stochastic and non-linear processes in nano-electro-mechanical systems.

Degree: Docteur es, Physique de la matière condensée et du rayonnement, 2018, Grenoble Alpes

URL: http://www.theses.fr/2018GREAY009

► Dans cette thèse, nous étudions des systèmes nano-électro-mécaniques (NEMS) en conditions cryogéniques (de 30 mK à 30 K) sensibles à des conditions de fluctuations ou… (more)

▼ Dans cette thèse, nous étudions des systèmes nano-électro-mécaniques (NEMS) en conditions cryogéniques (de 30 mK à 30 K) sensibles à des conditions de fluctuations ou de désordre. Les phénomènes fondamentaux étudiés sont omniprésents dans la physique des NEMS, et pour certains vont même au-delà avec des analogies vers d’autres disciplines de la physique, comme les transitions de phase ou la RMN.Dans la première partie de cette thèse, nous nous intéressons ainsi au bruit d'amplitude du NEMS, fournissant un exemple de mouvement Brownien dans un potentiel de confinement. Du fait de la non-linéarité géométrique intrinsèque au système, l'anharmonicité du potentiel transduit le mouvement Brownien de chaque mode mécanique en fluctuations des fréquences propres de résonance. Ainsi, nous observons expérimentalement un phénomène de diffusion spectrale, se traduisant par un élargissement et un décalage de la raie de résonance non-triviaux rendant compte de la compétition entre la diffusion de la phase de la réponse du mode due à la transduction, et les mécanismes de relaxation du mode fluctuant. Une approche par intégrale de chemin de la diffusion capture l'effet analytiquement. Un tel mécanisme altère la résonance d'un mode mécanique sans influer sur les échanges d'énergie avec le bain thermodynamique du mode. En outre, l'introduction d'une forte excitation sinusoïdale agit en retour sur les fluctuations hors équilibre via la non-linéarité, ralentissant la dynamique du système et comprimant ses fluctuations pour certains points critiques de l'espace des paramètres, près du ou dans le régime de la réponse bistable permise par la non-linéarité. Enfin, des expériences-modèles ont été réalisées afin de comprendre en détail la décohérence mécanique classique à l’aide d’un bruit en fréquence extrinsèque, réalisé à l’aide d’une grille couplée au NEMS.La deuxième partie de cette thèse explore plus en détail certains mécanismes microscopiques de relaxation d'énergie ou du bruit en fréquence interne d’un mode mécanique, encore partiellement incompris pour les NEMS. Nous considérons d’abord le cas d’une contribution extérieure, mais universelle, qui a pour origine le transfert d’impulsion entre le NEMS et le gaz présent dans la cellule expérimentale, ici l’hélium 4. Dans la limite des faibles densités, la théorie cinétique décrit la dissipation dans le gaz ballistique. De façon inattendue, nous observons aux plus basses pressions atteignables une déviation à la théorie. Nous montrons pour plusieurs températures et plusieurs échantillons que cette déviation s’échelonne avec le rapport entre le libre parcours moyen des atomes dans le gaz et la hauteur du NEMS vis-à-vis du fond de l’échantillon. Ce résultat est justifié par un modèle phénoménologique prenant en compte la réflexion diffusive des atomes du gaz sur le mur du fond, qui présente à petite échelle une structure désordonnée. Cette réflexion résulte en une déviation à la Maxwellienne près du fond, et donc en l’établissement d’un gradient de densité du gaz sur une longueur de l’ordre… Advisors/Committee Members: Collin, Eddy (thesis director).

Subjects/Keywords: Nanomécanique; Fluctuations; Non-Linéarité; Basses températures; Systèmes à deux niveaux; Nanomechanics; Fluctuations; Nonlinearity; Low temperatures; Two level systems; 530

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Maillet, O. (2018). Processus stochastiques et non-linéaires dans les systèmes nano-électro-mécaniques : Stochastic and non-linear processes in nano-electro-mechanical systems. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2018GREAY009

Chicago Manual of Style (16^th Edition):

Maillet, Olivier. “Processus stochastiques et non-linéaires dans les systèmes nano-électro-mécaniques : Stochastic and non-linear processes in nano-electro-mechanical systems.” 2018. Doctoral Dissertation, Grenoble Alpes. Accessed December 08, 2019. http://www.theses.fr/2018GREAY009.

MLA Handbook (7^th Edition):

Maillet, Olivier. “Processus stochastiques et non-linéaires dans les systèmes nano-électro-mécaniques : Stochastic and non-linear processes in nano-electro-mechanical systems.” 2018. Web. 08 Dec 2019.

Vancouver:

Maillet O. Processus stochastiques et non-linéaires dans les systèmes nano-électro-mécaniques : Stochastic and non-linear processes in nano-electro-mechanical systems. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2018. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2018GREAY009.

Council of Science Editors:

Maillet O. Processus stochastiques et non-linéaires dans les systèmes nano-électro-mécaniques : Stochastic and non-linear processes in nano-electro-mechanical systems. [Doctoral Dissertation]. Grenoble Alpes; 2018. Available from: http://www.theses.fr/2018GREAY009

Université de Sherbrooke

15. Normand-Lauzière, François. Anomalies postprandiales du métabolisme des acides gras libres dans l'évolution naturelle du diabète de type 2 .

Degree: 2010, Université de Sherbrooke

URL: http://hdl.handle.net/11143/5545

► Le diabète de type 2 (DM2) représente un fléau qui guette les habitants du continent nord-américain et est en lien avec l'épidémie d'obésité que nous… (more)

▼ Le diabète de type 2 (DM2) représente un fléau qui guette les habitants du continent nord-américain et est en lien avec l'épidémie d'obésité que nous vivons depuis quelques années. Cette pathologie à évolution lente fait intervenir deux éléments physiopathologiques cardinaux, soient la résistance à l'insuline (RI) et la diminution de sécrétion d'insuline en réponse au glucose (GSIS). Ces deux entités mèneront ultimement à l'hyperglycémie chronique par une atteinte directe de la régulation du métabolisme énergétique et de la physiologie de l'insuline. Plusieurs hypothèses de travail ont été mises de l'avant afin de tenter d'expliquer les dysfonctions métaboliques présentes chez ces patients. La surexposition des tissus non-adipeux aux acides gras libres (AGL), la lipotoxicité, est une candidate potentielle à l'identification d'une étiologie liée à la progression naturelle du diabète de type 2. Il est donc très pertinent d'étudier les effets lipotoxiques des AGL chez des sujets non-diabétiques enfants de deux parents diabétiques, qui sont à haut risque de développer cette pathologie. Notre équipe a récemment déterminé que ces sujets présentaient une augmentation de l'apparition des AGL plasmatiques ainsi qu'une augmentation de leur oxydation au cours d'une élévation expérimentales des niveaux d'AGL circulants. En conditions physiologiques, via l'ingestion d'un repas standardisé, les sujets non-diabétiques enfants de deux parents diabétiques, tout comme les sujets diabétiques, montrent une hypertriglycéridémie postprandiale qui n'est pas corrigée lorsque les niveaux de glucose sont contrôlés. De plus, ces sujets à haut risque de développer le DM2 ne se comportent pas comme les sujets diabétiques à ce qui à [i.e. a] trait aux niveaux d'apparition des AGL et de leur oxydation, comme il aurait été attendu. Les sujets diabétiques présentent une nette élévation des niveaux d'apparition et d'oxydation des AGL même si les niveaux de glucose sont corrigés. Ces anomalies semblent fortement associées avec la présence d'une adiposité abdominale chez les sujets DM2. Ces résultats expérimentaux suggèrent donc que les enfants de deux parents diabétiques ne possèdent pas encore les dysfonctions métaboliques qui mèneront ultimement à une surexposition des tissus non-adipeux aux acides gras libres via une augmentation de leur apparition plasmatique. Advisors/Committee Members: Carpentier, André (advisor).

Subjects/Keywords: Niveaux d'apparition; Oxydation; Acides gras libres; Tissu adipeux; Lipotoxicité; Résistance à l'insuline; Enfants de deux parents diabétiques; Diabète de type 2

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Normand-Lauzière, F. (2010). Anomalies postprandiales du métabolisme des acides gras libres dans l'évolution naturelle du diabète de type 2 . (Masters Thesis). Université de Sherbrooke. Retrieved from http://hdl.handle.net/11143/5545

Chicago Manual of Style (16^th Edition):

Normand-Lauzière, François. “Anomalies postprandiales du métabolisme des acides gras libres dans l'évolution naturelle du diabète de type 2 .” 2010. Masters Thesis, Université de Sherbrooke. Accessed December 08, 2019. http://hdl.handle.net/11143/5545.

MLA Handbook (7^th Edition):

Normand-Lauzière, François. “Anomalies postprandiales du métabolisme des acides gras libres dans l'évolution naturelle du diabète de type 2 .” 2010. Web. 08 Dec 2019.

Vancouver:

Normand-Lauzière F. Anomalies postprandiales du métabolisme des acides gras libres dans l'évolution naturelle du diabète de type 2 . [Internet] [Masters thesis]. Université de Sherbrooke; 2010. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/11143/5545.

Council of Science Editors:

Normand-Lauzière F. Anomalies postprandiales du métabolisme des acides gras libres dans l'évolution naturelle du diabète de type 2 . [Masters Thesis]. Université de Sherbrooke; 2010. Available from: http://hdl.handle.net/11143/5545

16. Teyssier, Geoffrey. Inequality of opportunity : measurement and impact on economic growth : Inégalité d'opportunité : mesure et effet sur la croissance économique.

Degree: Docteur es, Sciences économiques, 2017, Paris 1

URL: http://www.theses.fr/2017PA01E060

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Cette thèse porte sur la mesure de l'inégalité d'opportunité et son effet sur la croissance économique. Le Chapitre 1 étudie les propriétés axiomatiques de deux… (more)

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Cette thèse porte sur la mesure de l'inégalité d'opportunité et son effet sur la croissance économique. Le Chapitre 1 étudie les propriétés axiomatiques de deux approches de mesure concurrentes. Dans les deux cas, la population est partitionnée en groupes rassemblant des personnes partageant les mêmes circonstances, ces déterminants de revenu que les individus ne peuvent choisir (ex. sexe ou milieu familial). L'inégalité d'opportunité est alors mesurée comme celle présente au sein d'une distribution contrefactuelle où chacun se voit attribuer le revenu représentatif de son groupe. La première approche considère la moyenne arithmétique comme revenu représentatif. Lorsque le nombre de groupes est grand et que leur taille est petite, ces moyennes sont peu précisément estimées. Afin de d'atténuer ce problème, la seconde approche, dite paramétrique, suppose que les circonstances n'ont pas d'effet d'interaction et remplace la moyenne arithmétique par la prédiction OLS du revenu régressé sur les circonstances. Le Chapitre 1 montre que la méthode paramétrique est faible d'un point de vue axiomatique. En particulier, elle ne respecte pas une version «entre-groupes» du principe des transferts. Le Chapitre 2 propose une méthodologie afin de contourner le manque actuel de micro-données sur les circonstances parentales, un déterminant majeur de l'inégalité d'opportunité. L'idée est d'utiliser 1 structure des enquêtes démographiques organisées autour de foyers afin de retrouver les circonstances parentales des adultes vivant avec leurs parents, puis d'utiliser une méthode d'ajustement statistique -l'imputation multiple -afin d'obtenir une mesure d'inégalité d'opportunité représentative de la population adulte dans son ensemble. Celle-ci est proche de la« vraie» inégalité d'opportunité, qui repose sur des questions directes à propos du milieu parental contenue dans l'enquête brésilienne du PNAD 1996. Le Chapitre 3 étudie empiriquement une récente explication quant au caractère peu concluant de la littérature empirique sur l'inégalité et la croissance: ce n'est pas l'inégalité de revenus qui compte pour la croissance mais ses deux composantes, à savoir l'inégalité d'opportunité et la composante résiduelle qu'est l'inégalité d'effort. Cette explication est validée au Brésil au niveau municipal durant la période 1980-2010, où le: inégalités d'opportunité et d'effort sont respectivement préjudiciables et bénéfiques à la croissance économique future, comme attendu. Leurs effets sont robustes et significatifs, contrairement à celui de l'inégalité total de revenus.

This thesis is about the measurement of inequality of opportunity and its impact on economic growth. Chapter 1 studies the axiomatic properties of two prominent measurement approaches. In both cases, the population is partitioned into groups of people sharing the same circumstances, those income determinants that are beyond individual control (e.g. sex or parental background) and that shape one's opportunities. Inequality of opportunity is then measured by applying a1…

Advisors/Committee Members: Poncet, Sandra (thesis director).

Subjects/Keywords: Inégalité de revenus; Inégalité d'opportunité; Croissance économique; Imputation multiple; Income inequality; Inequality of opportunity; Economic growth; Multiple imputation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Teyssier, G. (2017). Inequality of opportunity : measurement and impact on economic growth : Inégalité d'opportunité : mesure et effet sur la croissance économique. (Doctoral Dissertation). Paris 1. Retrieved from http://www.theses.fr/2017PA01E060

Chicago Manual of Style (16^th Edition):

Teyssier, Geoffrey. “Inequality of opportunity : measurement and impact on economic growth : Inégalité d'opportunité : mesure et effet sur la croissance économique.” 2017. Doctoral Dissertation, Paris 1. Accessed December 08, 2019. http://www.theses.fr/2017PA01E060.

MLA Handbook (7^th Edition):

Teyssier, Geoffrey. “Inequality of opportunity : measurement and impact on economic growth : Inégalité d'opportunité : mesure et effet sur la croissance économique.” 2017. Web. 08 Dec 2019.

Vancouver:

Teyssier G. Inequality of opportunity : measurement and impact on economic growth : Inégalité d'opportunité : mesure et effet sur la croissance économique. [Internet] [Doctoral dissertation]. Paris 1; 2017. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2017PA01E060.

Council of Science Editors:

Teyssier G. Inequality of opportunity : measurement and impact on economic growth : Inégalité d'opportunité : mesure et effet sur la croissance économique. [Doctoral Dissertation]. Paris 1; 2017. Available from: http://www.theses.fr/2017PA01E060

Université Paris-Sud – Paris XI

17. Marti soler, Helena. Modélisation des données d'enquêtes cas-cohorte par imputation multiple : application en épidémiologie cardio-vasculaire : Modeling of case-cohort data by multiple imputation : application to cardio-vascular epidemiology.

Degree: Docteur es, Biostatistiques, 2012, Université Paris-Sud – Paris XI

URL: http://www.theses.fr/2012PA11T022

► Les estimateurs pondérés généralement utilisés pour analyser les enquêtes cas-cohorte ne sont pas pleinement efficaces. Or, les enquêtes cas-cohorte sont un cas particulier de données… (more)

▼ Les estimateurs pondérés généralement utilisés pour analyser les enquêtes cas-cohorte ne sont pas pleinement efficaces. Or, les enquêtes cas-cohorte sont un cas particulier de données incomplètes où le processus d'observation est contrôlé par les organisateurs de l'étude. Ainsi, des méthodes d'analyse pour données manquant au hasard (MA) peuvent être pertinentes, en particulier, l'imputation multiple, qui utilise toute l'information disponible et permet d'approcher l'estimateur du maximum de vraisemblance partielle.Cette méthode est fondée sur la génération de plusieurs jeux plausibles de données complétées prenant en compte les différents niveaux d'incertitude sur les données manquantes. Elle permet d'adapter facilement n'importe quel outil statistique disponible pour les données de cohorte, par exemple, l'estimation de la capacité prédictive d'un modèle ou d'une variable additionnelle qui pose des problèmes spécifiques dans les enquêtes cas-cohorte. Nous avons montré que le modèle d'imputation doit être estimé à partir de tous les sujets complètement observés (cas et non-cas) en incluant l'indicatrice de statut parmi les variables explicatives. Nous avons validé cette approche à l'aide de plusieurs séries de simulations: 1) données complètement simulées, où nous connaissions les vraies valeurs des paramètres, 2) enquêtes cas-cohorte simulées à partir de la cohorte PRIME, où nous ne disposions pas d'une variable de phase-1 (observée sur tous les sujets) fortement prédictive de la variable de phase-2 (incomplètement observée), 3) enquêtes cas-cohorte simulées à partir de la cohorte NWTS, où une variable de phase-1 fortement prédictive de la variable de phase-2 était disponible. Ces simulations ont montré que l'imputation multiple fournissait généralement des estimateurs sans biais des risques relatifs. Pour les variables de phase-1, ils approchaient la précision obtenue par l'analyse de la cohorte complète, ils étaient légèrement plus précis que l'estimateur calibré de Breslow et coll. et surtout que les estimateurs pondérés classiques. Pour les variables de phase-2, l'estimateur de l'imputation multiple était généralement sans biais et d'une précision supérieure à celle des estimateurs pondérés classiques et analogue à celle de l'estimateur calibré. Les résultats des simulations réalisées à partir des données de la cohorte NWTS étaient cependant moins bons pour les effets impliquant la variable de phase-2 : les estimateurs de l'imputation multiple étaient légèrement biaisés et moins précis que les estimateurs pondérés. Cela s'explique par la présence de termes d'interaction impliquant la variable de phase-2 dans le modèle d'analyse, d'où la nécessité d'estimer des modèles d'imputation spécifiques à différentes strates de la cohorte incluant parfois trop peu de cas pour que les conditions asymptotiques soient réunies.Nous recommandons d'utiliser l'imputation multiple pour obtenir des estimations plus précises des risques relatifs, tout en s'assurant qu'elles sont analogues à celles fournies par les analyses… Advisors/Committee Members: Chavance, Michel (thesis director).

Subjects/Keywords: Enquêtes cas-cohorte; Estimateurs pondérés; Imputation multiple; Capacité prédictive; Case-cohort surveys; Weighted estimators; Multiple imputation; Predictive ability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Marti soler, H. (2012). Modélisation des données d'enquêtes cas-cohorte par imputation multiple : application en épidémiologie cardio-vasculaire : Modeling of case-cohort data by multiple imputation : application to cardio-vascular epidemiology. (Doctoral Dissertation). Université Paris-Sud – Paris XI. Retrieved from http://www.theses.fr/2012PA11T022

Chicago Manual of Style (16^th Edition):

Marti soler, Helena. “Modélisation des données d'enquêtes cas-cohorte par imputation multiple : application en épidémiologie cardio-vasculaire : Modeling of case-cohort data by multiple imputation : application to cardio-vascular epidemiology.” 2012. Doctoral Dissertation, Université Paris-Sud – Paris XI. Accessed December 08, 2019. http://www.theses.fr/2012PA11T022.

MLA Handbook (7^th Edition):

Marti soler, Helena. “Modélisation des données d'enquêtes cas-cohorte par imputation multiple : application en épidémiologie cardio-vasculaire : Modeling of case-cohort data by multiple imputation : application to cardio-vascular epidemiology.” 2012. Web. 08 Dec 2019.

Vancouver:

Marti soler H. Modélisation des données d'enquêtes cas-cohorte par imputation multiple : application en épidémiologie cardio-vasculaire : Modeling of case-cohort data by multiple imputation : application to cardio-vascular epidemiology. [Internet] [Doctoral dissertation]. Université Paris-Sud – Paris XI; 2012. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2012PA11T022.

Council of Science Editors:

Marti soler H. Modélisation des données d'enquêtes cas-cohorte par imputation multiple : application en épidémiologie cardio-vasculaire : Modeling of case-cohort data by multiple imputation : application to cardio-vascular epidemiology. [Doctoral Dissertation]. Université Paris-Sud – Paris XI; 2012. Available from: http://www.theses.fr/2012PA11T022

Université de Montréal

18. Vallée, Audrey-Anne. Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie .

Degree: 2014, Université de Montréal

URL: http://hdl.handle.net/1866/11120

► Les travaux portent sur l’estimation de la variance dans le cas d’une non- réponse partielle traitée par une procédure d’imputation. Traiter les valeurs imputées comme… (more)

Subjects/Keywords: Imputation; Non-réponse; Estimation de la variance; Entropie d'un plan de sondage; Probabilités d'inclusion d'ordre deux; Imputation; Nonresponse; Variance estimation; Entropy of a sampling design; Second-order inclusion probabilities

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APA (6^th Edition):

Vallée, A. (2014). Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/11120

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vallée, Audrey-Anne. “Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie .” 2014. Thesis, Université de Montréal. Accessed December 08, 2019. http://hdl.handle.net/1866/11120.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vallée, Audrey-Anne. “Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie .” 2014. Web. 08 Dec 2019.

Vancouver:

Vallée A. Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie . [Internet] [Thesis]. Université de Montréal; 2014. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1866/11120.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vallée A. Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie . [Thesis]. Université de Montréal; 2014. Available from: http://hdl.handle.net/1866/11120

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago

19. Helenowski, Irene B. Multiple Imputation via a Semi-Parametric Probability Integral Transformation.

Degree: 2012, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/8652

► In real data scenarios, the distribution of the data is often unknown. Therefore, methods for imputing data which relax distributional or model assumptions may be… (more)

Subjects/Keywords: multiple imputation; random number generation; empirical cumulative distribution function

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APA (6^th Edition):

Helenowski, I. B. (2012). Multiple Imputation via a Semi-Parametric Probability Integral Transformation. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/8652

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Helenowski, Irene B. “Multiple Imputation via a Semi-Parametric Probability Integral Transformation.” 2012. Thesis, University of Illinois – Chicago. Accessed December 08, 2019. http://hdl.handle.net/10027/8652.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Helenowski, Irene B. “Multiple Imputation via a Semi-Parametric Probability Integral Transformation.” 2012. Web. 08 Dec 2019.

Vancouver:

Helenowski IB. Multiple Imputation via a Semi-Parametric Probability Integral Transformation. [Internet] [Thesis]. University of Illinois – Chicago; 2012. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10027/8652.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Helenowski IB. Multiple Imputation via a Semi-Parametric Probability Integral Transformation. [Thesis]. University of Illinois – Chicago; 2012. Available from: http://hdl.handle.net/10027/8652

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago

20. Nieh, Chiping. Using Mass Balance, Factor Analysis, and Multiple Imputation to Assess Health Effects of Water Quality.

Degree: 2012, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/8658

► This dissertation explores the use of three analytical methods to improve the utility of microbial water quality data, collected on the Chicago River from 2007… (more)

Subjects/Keywords: Multiple imputation; factor analysis; chemical mass balance model; surface water quality

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APA (6^th Edition):

Nieh, C. (2012). Using Mass Balance, Factor Analysis, and Multiple Imputation to Assess Health Effects of Water Quality. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/8658

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nieh, Chiping. “Using Mass Balance, Factor Analysis, and Multiple Imputation to Assess Health Effects of Water Quality.” 2012. Thesis, University of Illinois – Chicago. Accessed December 08, 2019. http://hdl.handle.net/10027/8658.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nieh, Chiping. “Using Mass Balance, Factor Analysis, and Multiple Imputation to Assess Health Effects of Water Quality.” 2012. Web. 08 Dec 2019.

Vancouver:

Nieh C. Using Mass Balance, Factor Analysis, and Multiple Imputation to Assess Health Effects of Water Quality. [Internet] [Thesis]. University of Illinois – Chicago; 2012. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10027/8658.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nieh C. Using Mass Balance, Factor Analysis, and Multiple Imputation to Assess Health Effects of Water Quality. [Thesis]. University of Illinois – Chicago; 2012. Available from: http://hdl.handle.net/10027/8658

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Washington University in St. Louis

21. Murden, Raphiel. Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders.

Degree: MA, Mathematics, 2011, Washington University in St. Louis

URL: https://openscholarship.wustl.edu/etd/496

► Women in the United States are becoming infected with HIV more quickly now than ever before; many of whom are at higher risk because of… (more)

Subjects/Keywords: Statistics; Missing data, Multiple Imputation, HIV, Substance Use Disorder, Women

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APA (6^th Edition):

Murden, R. (2011). Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders. (Thesis). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/etd/496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Murden, Raphiel. “Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders.” 2011. Thesis, Washington University in St. Louis. Accessed December 08, 2019. https://openscholarship.wustl.edu/etd/496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Murden, Raphiel. “Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders.” 2011. Web. 08 Dec 2019.

Vancouver:

Murden R. Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders. [Internet] [Thesis]. Washington University in St. Louis; 2011. [cited 2019 Dec 08]. Available from: https://openscholarship.wustl.edu/etd/496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Murden R. Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders. [Thesis]. Washington University in St. Louis; 2011. Available from: https://openscholarship.wustl.edu/etd/496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

North-West University

22. Liebenberg, Shawn Carl. Multiple imputation in the presence of a detection limit, with applications : an empirical approach / Shawn Carl Liebenberg .

Degree: 2014, North-West University

URL: http://hdl.handle.net/10394/15363

► Scientists often encounter unobserved or missing measurements that are typically reported as less than a fixed detection limit. This especially occurs in the environmental sciences… (more)

▼ Scientists often encounter unobserved or missing measurements that are typically reported as less than a fixed detection limit. This especially occurs in the environmental sciences when detection of low exposures are not possible due to limitations of the measuring instrument, and the resulting data are often referred to as type I and II left censored data. Observations lying below this detection limit are therefore often ignored, or `guessed' because it cannot be measured accurately. However, reliable estimates of the population parameters are nevertheless required to perform statistical analysis. The problem of dealing with values below a detection limit becomes increasingly complex when a large number of observations are present below this limit. Researchers thus have interest in developing statistical robust estimation procedures for dealing with left- or right-censored data sets (SinghandNocerino2002). The aim of this study focuses on several main components regarding the problems mentioned above. The imputation of censored data below a fixed detection limit are studied, particularly using the maximum likelihood procedure of Cohen(1959), and several variants thereof, in combination with four new variations of the multiple imputation concept found in literature. Furthermore, the focus also falls strongly on estimating the density of the resulting imputed, `complete' data set by applying various kernel density estimators. It should be noted that bandwidth selection issues are not of importance in this study, and will be left for further research. In this study, however, the maximum likelihood estimation method of Cohen (1959) will be compared with several variant methods, to establish which of these maximum likelihood estimation procedures for censored data estimates the population parameters of three chosen Lognormal distribution, the most reliably in terms of well-known discrepancy measures. These methods will be implemented in combination with four new multiple imputation procedures, respectively, to assess which of these nonparametric methods are most effective with imputing the 12 censored values below the detection limit, with regards to the global discrepancy measures mentioned above. Several variations of the Parzen-Rosenblatt kernel density estimate will be fitted to the complete filled-in data sets, obtained from the previous methods, to establish which is the preferred data-driven method to estimate these densities. The primary focus of the current study will therefore be the performance of the four chosen multiple imputation methods, as well as the recommendation of methods and procedural combinations to deal with data in the presence of a detection limit. An extensive Monte Carlo simulation study was performed to compare the various methods and procedural combinations. Conclusions and recommendations regarding the best of these methods and combinations are made based on the study's results.

Subjects/Keywords: Multiple imputation; Detection limit; Maximum likelihood estimation; Kernel density estimation; Bootstrap

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liebenberg, S. C. (2014). Multiple imputation in the presence of a detection limit, with applications : an empirical approach / Shawn Carl Liebenberg . (Thesis). North-West University. Retrieved from http://hdl.handle.net/10394/15363

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liebenberg, Shawn Carl. “Multiple imputation in the presence of a detection limit, with applications : an empirical approach / Shawn Carl Liebenberg .” 2014. Thesis, North-West University. Accessed December 08, 2019. http://hdl.handle.net/10394/15363.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liebenberg, Shawn Carl. “Multiple imputation in the presence of a detection limit, with applications : an empirical approach / Shawn Carl Liebenberg .” 2014. Web. 08 Dec 2019.

Vancouver:

Liebenberg SC. Multiple imputation in the presence of a detection limit, with applications : an empirical approach / Shawn Carl Liebenberg . [Internet] [Thesis]. North-West University; 2014. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10394/15363.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liebenberg SC. Multiple imputation in the presence of a detection limit, with applications : an empirical approach / Shawn Carl Liebenberg . [Thesis]. North-West University; 2014. Available from: http://hdl.handle.net/10394/15363

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

23. Medhanie, Amanuel Gebri. The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models.

Degree: PhD, Educational Psychology, 2013, University of Minnesota

URL: http://purl.umn.edu/155986

► Missing data often present problems for credible statistical analyses. Luckily there are valid methods for dealing with missing data but the context in which the… (more)

Subjects/Keywords: Hierarchical linear models; Missing data; Multilevel; Multiple imputation; Nested data; PAN

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APA (6^th Edition):

Medhanie, A. G. (2013). The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/155986

Chicago Manual of Style (16^th Edition):

Medhanie, Amanuel Gebri. “The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models.” 2013. Doctoral Dissertation, University of Minnesota. Accessed December 08, 2019. http://purl.umn.edu/155986.

MLA Handbook (7^th Edition):

Medhanie, Amanuel Gebri. “The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models.” 2013. Web. 08 Dec 2019.

Vancouver:

Medhanie AG. The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2019 Dec 08]. Available from: http://purl.umn.edu/155986.

Council of Science Editors:

Medhanie AG. The robustness of multilevel multiple imputation for handling missing data in hierarchical linear models. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/155986

University of Rochester

24. Hebert, Donald Joseph. Global Tests for Multiple Outcomes in Randomized Trials.

Degree: PhD, 2016, University of Rochester

URL: http://hdl.handle.net/1802/31567

► Methods for analyses of multiple outcome variables in randomized trials are prevalent throughout the statistical literature. Research for the case where the interest is in… (more)

Subjects/Keywords: Global testing; Multiples outcomes; Missing data; Linear combinations; Multiple imputation

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APA (6^th Edition):

Hebert, D. J. (2016). Global Tests for Multiple Outcomes in Randomized Trials. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/31567

Chicago Manual of Style (16^th Edition):

Hebert, Donald Joseph. “Global Tests for Multiple Outcomes in Randomized Trials.” 2016. Doctoral Dissertation, University of Rochester. Accessed December 08, 2019. http://hdl.handle.net/1802/31567.

MLA Handbook (7^th Edition):

Hebert, Donald Joseph. “Global Tests for Multiple Outcomes in Randomized Trials.” 2016. Web. 08 Dec 2019.

Vancouver:

Hebert DJ. Global Tests for Multiple Outcomes in Randomized Trials. [Internet] [Doctoral dissertation]. University of Rochester; 2016. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1802/31567.

Council of Science Editors:

Hebert DJ. Global Tests for Multiple Outcomes in Randomized Trials. [Doctoral Dissertation]. University of Rochester; 2016. Available from: http://hdl.handle.net/1802/31567

University of Adelaide

25. Sullivan, Thomas Richard. Multiple Imputation for Handling Missing Outcome Data.

Degree: 2017, University of Adelaide

URL: http://hdl.handle.net/2440/119248

► Background: Multiple imputation is a widely used approach to handling missing data. Despite a growing evidence base for its use, implementation in practical settings remains… (more)

Subjects/Keywords: Multiple imputation; missing data; missing at random; clinical trials

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sullivan, T. R. (2017). Multiple Imputation for Handling Missing Outcome Data. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/119248

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sullivan, Thomas Richard. “Multiple Imputation for Handling Missing Outcome Data.” 2017. Thesis, University of Adelaide. Accessed December 08, 2019. http://hdl.handle.net/2440/119248.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sullivan, Thomas Richard. “Multiple Imputation for Handling Missing Outcome Data.” 2017. Web. 08 Dec 2019.

Vancouver:

Sullivan TR. Multiple Imputation for Handling Missing Outcome Data. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2440/119248.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sullivan TR. Multiple Imputation for Handling Missing Outcome Data. [Thesis]. University of Adelaide; 2017. Available from: http://hdl.handle.net/2440/119248

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Arizona State University

26. Mistler, Stephen Andrew. Multilevel multiple imputation: An examination of competing methods.

Degree: Psychology, 2015, Arizona State University

URL: http://repository.asu.edu/items/29655

► Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing… (more)

Subjects/Keywords: Statistics; Psychology; Hierarchical; Missing Data; Multilevel Modeling; Multiple Imputation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mistler, S. A. (2015). Multilevel multiple imputation: An examination of competing methods. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/29655

Chicago Manual of Style (16^th Edition):

Mistler, Stephen Andrew. “Multilevel multiple imputation: An examination of competing methods.” 2015. Doctoral Dissertation, Arizona State University. Accessed December 08, 2019. http://repository.asu.edu/items/29655.

MLA Handbook (7^th Edition):

Mistler, Stephen Andrew. “Multilevel multiple imputation: An examination of competing methods.” 2015. Web. 08 Dec 2019.

Vancouver:

Mistler SA. Multilevel multiple imputation: An examination of competing methods. [Internet] [Doctoral dissertation]. Arizona State University; 2015. [cited 2019 Dec 08]. Available from: http://repository.asu.edu/items/29655.

Council of Science Editors:

Mistler SA. Multilevel multiple imputation: An examination of competing methods. [Doctoral Dissertation]. Arizona State University; 2015. Available from: http://repository.asu.edu/items/29655

University of Michigan

27. Beesley, Lauren. Missing Data and Variable Selection Methods for Cure Models in Cancer Research.

Degree: PhD, Biostatistics, 2018, University of Michigan

URL: http://hdl.handle.net/2027.42/144010

► In survival analysis, a common assumption is that all subjects will eventually experience the event of interest given long enough follow-up time. However, there are… (more)

▼ In survival analysis, a common assumption is that all subjects will eventually experience the event of interest given long enough follow-up time. However, there are many settings in which this assumption does not hold. For example, suppose we are interested in studying cancer recurrence. If the treatment eradicated the cancer for some patients, then there will be a subset of the population that will never experience a recurrence. We call these subjects “cured.” The Cox proportional hazards (CPH) mixture cure model and a generalization, the multistate cure model, can be used to model time-to-event outcomes in the cure setting. In this dissertation, we will address issues of missing data, variable selection, and parameter estimation for these models. We will also explore issues of missing covariate and outcome data for a more general class of models, of which cure models are a particular case. In Chapter II, we propose several chained equations methods for imputing missing covariates under the CPH mixture cure model, and we compare the novel approaches with existing chained equations methods for imputing survival data without a cured fraction. In Chapter III, we develop sequential imputation methods for a general class of models with latent and partially latent variables (of which cure models are an example). In particular, we consider the setting where covariate/outcome missingness depends on the latent variable, which is a missing not at random mechanism. In Chapter IV, we develop an EM algorithm for fitting the multistate cure model. The existing method for fitting this model requires custom software and can be slow to converge. In contrast, the proposed method can be easily implemented using standard software and typically converges quickly. We further propose a Monte Carlo EM algorithm for fitting the multistate cure model in the presence of covariate missingness and/or unequal censoring of the outcomes. In Chapter V, we propose a generalization of the multistate cure model to incorporate subjects with persistent disease. This model has many parameters, and variable selection/shrinkage methods are needed to aid in estimation. We compare the performance of existing variable selection/shrinkage methods in estimating model parameters for a study of head and neck cancer. In Chapter VI, we develop Bayesian methods for performing variable selection when we have order restrictions for model parameters. In particular, we consider the setting in which we have interactions with one or more order-restricted variables. A simulation study demonstrates promising properties of the proposed selection method. Advisors/Committee Members: Taylor, Jeremy Michael George (committee member), Rozek, Laura Marie (committee member), Braun, Thomas M (committee member), Little, Roderick J (committee member), Schipper, Matthew Jason (committee member).

Subjects/Keywords: cure models; multiple imputation; cancer modeling; Public Health; Health Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Beesley, L. (2018). Missing Data and Variable Selection Methods for Cure Models in Cancer Research. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/144010

Chicago Manual of Style (16^th Edition):

Beesley, Lauren. “Missing Data and Variable Selection Methods for Cure Models in Cancer Research.” 2018. Doctoral Dissertation, University of Michigan. Accessed December 08, 2019. http://hdl.handle.net/2027.42/144010.

MLA Handbook (7^th Edition):

Beesley, Lauren. “Missing Data and Variable Selection Methods for Cure Models in Cancer Research.” 2018. Web. 08 Dec 2019.

Vancouver:

Beesley L. Missing Data and Variable Selection Methods for Cure Models in Cancer Research. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2027.42/144010.

Council of Science Editors:

Beesley L. Missing Data and Variable Selection Methods for Cure Models in Cancer Research. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/144010

28. Imbert, Alyssa. Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées : Integrating heterogeneous complex data from unbalanced datasets.

Degree: Docteur es, Mathématiques appliquées, 2018, Université Toulouse I – Capitole

URL: http://www.theses.fr/2018TOU10022

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Les avancées des nouvelles technologies de séquençage ont permis aux études cliniques de produire des données volumineuses et complexes. Cette complexité se décline selon diverses… (more)

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Les avancées des nouvelles technologies de séquençage ont permis aux études cliniques de produire des données volumineuses et complexes. Cette complexité se décline selon diverses modalités, notamment la grande dimension, l’hétérogénéité des données au niveau biologique (acquises à différents niveaux de l’échelle du vivant et à divers moments de l’expérience), l’hétérogénéité du type de données, le bruit (hétérogénéité biologique ou données entachées d’erreurs) dans les données et la présence de données manquantes (au niveau d’une valeur ou d’un individu entier). L’intégration de différentes données est donc un défi important pour la biologie computationnelle. Cette thèse s’inscrit dans un projet de recherche clinique sur l’obésité, DiOGenes, pour lequel nous avons fait des propositions méthodologiques pour l’analyse et l’intégration de données. Ce projet est basé sur une intervention nutritionnelle menée dans huit pays européens et vise à analyser les effets de différents régimes sur le maintien pondéral et sur certains marqueurs de risque cardio-vasculaire et de diabète, chez des individus obèses. Dans le cadre de ce projet, mes travaux ont porté sur l’analyse de données transcriptomiques (RNA-Seq) avec des individus manquants et sur l’intégration de données transcriptomiques (nouvelle technique QuantSeq) avec des données cliniques. La première partie de cette thèse est consacrée aux données manquantes et à l’inférence de réseaux à partir de données d’expression RNA-Seq. Lors d’études longitudinales transcriptomiques, il arrive que certains individus ne soient pas observés à certains pas de temps, pour des raisons expérimentales. Nous proposons une méthode d’imputation multiple hot-deck (hd-MI) qui permet d’intégrer de l’information externe mesurée sur les mêmes individus et d’autres individus. hd-MI permet d’améliorer la qualité de l’inférence de réseau. La seconde partie porte sur une étude intégrative de données cliniques et transcriptomiques (mesurées par QuantSeq) basée sur une approche réseau. Nous y montrons l’intérêt de cette nouvelle technique pour l’acquisition de données transcriptomiques et l’analysons par une approche d’inférence de réseau en lien avec des données cliniques d’intérêt.

The development of high-throughput sequencing technologies has lead to a massive acquisition of high dimensional and complex datasets. Different features make these datasets hard to analyze : high dimensionality, heterogeneity at the biological level or at the data type level, the noise in data (due to biological heterogeneity or to errors in data) and the presence of missing data (for given values or for an entire individual). The integration of various data is thus an important challenge for computational biology. This thesis is part of a large clinical research project on obesity, DiOGenes, in which we have developed methods for data analysis and integration. The project is based on a dietary intervention that was led in eight Europeans centers. This study investigated the effect of macronutrient composition on…

Advisors/Committee Members: Villa-Vialaneix, Nathalie (thesis director), Viguerie, Nathalie (thesis director).

Subjects/Keywords: Analyse de données transcriptomiques; Imputation multiple hot-deck

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Imbert, A. (2018). Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées : Integrating heterogeneous complex data from unbalanced datasets. (Doctoral Dissertation). Université Toulouse I – Capitole. Retrieved from http://www.theses.fr/2018TOU10022

Chicago Manual of Style (16^th Edition):

Imbert, Alyssa. “Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées : Integrating heterogeneous complex data from unbalanced datasets.” 2018. Doctoral Dissertation, Université Toulouse I – Capitole. Accessed December 08, 2019. http://www.theses.fr/2018TOU10022.

MLA Handbook (7^th Edition):

Imbert, Alyssa. “Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées : Integrating heterogeneous complex data from unbalanced datasets.” 2018. Web. 08 Dec 2019.

Vancouver:

Imbert A. Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées : Integrating heterogeneous complex data from unbalanced datasets. [Internet] [Doctoral dissertation]. Université Toulouse I – Capitole; 2018. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2018TOU10022.

Council of Science Editors:

Imbert A. Intégration de données hétérogènes complexes à partir de tableaux de tailles déséquilibrées : Integrating heterogeneous complex data from unbalanced datasets. [Doctoral Dissertation]. Université Toulouse I – Capitole; 2018. Available from: http://www.theses.fr/2018TOU10022

University of Arizona

29. Zhou, Muhan. A Nearest-Neighbor Nonparametric Multiple Imputation Approach for Incomplete Categorical Data under Missing at Random .

Degree: 2019, University of Arizona

URL: http://hdl.handle.net/10150/634211

► Incomplete categorical data is a common problem in medical research. If researchers simply use complete cases for data analysis, the estimation might be biased and/or… (more)

▼ Incomplete categorical data is a common problem in medical research. If researchers simply use complete cases for data analysis, the estimation might be biased and/or inefficient due to ignoring the missing values. Under the assumption of missing at random (MAR), i.e. missing values depend only on the observed data but not on the unobserved data, an increasing number of approaches have been proposed to handle missing data. However, most of the existing missing-data methods for incomplete categorical data are either not robust or sensitive to extreme missingness probabilities. In my dissertation, I study a nearest-neighbor nonparametric multiple imputation approach (NNMI) using two working models to impute values for a missing at random categorical variable, and to estimate marginal mean as well as conditional mean under three different study designs. In the first paper, I adopt the NNMI for dealing with a categorical outcome with missing values and estimating the proportion of each category. Specifically, multinomial logistic regression/cumulative logistic regression is performed to construct a working model for predicting the incomplete categorical outcome. Logistic regression is performed to fit a working model for predicting the missingness probabilities. The predicted values from the two working models are used as scores for calculating distances between each missing value with other non-missing values. A weighting scheme is used to accommodate contributions from two working models when generating predictive scores. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances (donors) from each of the missing observations. I conduct a simulation study to evaluate the performance of the NNMI method and compare it with several alternative methods. A real-data application is presented using a dataset from the 2013 Behavioral Risk Factor Surveillance System (BRFSS) survey. In the second paper, I use the NNMI method to handle missing covariate in logistic regression. Similarly, two working models are used to predict the incomplete covariate and the missingness probabilities. First, I perform a computation to assess the potential factors related to selecting an optimal size of donors. Second, the performance of the proposed method is compared with several alternative methods. Finally, the NNMI is applied on the 2013 BRFSS survey data to impute an incomplete categorical covariate and estimate the regression coefficients from a logistic regression model. In the third paper, the NNMI is extended to handle missing covariate under a matched case-control study. The estimation is conducted using a conditional logistic regression model. The performance of the NNMI is compared with complete cases and six parametric multiple imputation methods. The objective is to assess whether the NNMI demonstrates a doubly robust property compared with parametric methods. Then the NNMI is applied to impute an incomplete categorical covariate under a nested case-control cohort using the 2013 BRFSS… Advisors/Committee Members: Hsu, Chiu-Hsieh (advisor), Bell, Melanie L. (committeemember), Guerra, Stefano (committeemember), Hu, Chengcheng (committeemember).

Subjects/Keywords: Categorical data; Double Robustness; Missing data; Multiple imputation; Nearest Neighbor

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhou, M. (2019). A Nearest-Neighbor Nonparametric Multiple Imputation Approach for Incomplete Categorical Data under Missing at Random . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/634211

Chicago Manual of Style (16^th Edition):

Zhou, Muhan. “A Nearest-Neighbor Nonparametric Multiple Imputation Approach for Incomplete Categorical Data under Missing at Random .” 2019. Doctoral Dissertation, University of Arizona. Accessed December 08, 2019. http://hdl.handle.net/10150/634211.

MLA Handbook (7^th Edition):

Zhou, Muhan. “A Nearest-Neighbor Nonparametric Multiple Imputation Approach for Incomplete Categorical Data under Missing at Random .” 2019. Web. 08 Dec 2019.

Vancouver:

Zhou M. A Nearest-Neighbor Nonparametric Multiple Imputation Approach for Incomplete Categorical Data under Missing at Random . [Internet] [Doctoral dissertation]. University of Arizona; 2019. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10150/634211.

Council of Science Editors:

Zhou M. A Nearest-Neighbor Nonparametric Multiple Imputation Approach for Incomplete Categorical Data under Missing at Random . [Doctoral Dissertation]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/634211

Duke University

30. Si, Yajuan. Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies .

Degree: 2012, Duke University

URL: http://hdl.handle.net/10161/5837

► The thesis develops nonparametric Bayesian models to handle incomplete categorical variables in data sets with high dimension using the framework of multiple imputation. It… (more)

▼ The thesis develops nonparametric Bayesian models to handle incomplete categorical variables in data sets with high dimension using the framework of multiple imputation. It presents methods for ignorable missing data in cross-sectional studies, and potentially non-ignorable missing data in panel studies with refreshment samples. The first contribution is a fully Bayesian, joint modeling approach of multiple imputation for categorical data based on Dirichlet process mixtures of multinomial distributions. The approach automatically models complex dependencies while being computationally expedient. I illustrate repeated sampling properties of the approach using simulated data. This approach offers better performance than default chained equations methods, which are often used in such settings. I apply the methodology to impute missing background data in the 2007 Trends in International Mathematics and Science Study. For the second contribution, I extend the nonparametric Bayesian imputation engine to consider a mix of potentially non-ignorable attrition and ignorable item nonresponse in multiple wave panel studies. Ignoring the attrition in models for panel data can result in biased inference if the reason for attrition is systematic and related to the missing values. Panel data alone cannot estimate the attrition effect without untestable assumptions about the missing data mechanism. Refreshment samples offer an extra data source that can be utilized to estimate the attrition effect while reducing reliance on strong assumptions of the missing data mechanism. I consider two novel Bayesian approaches to handle the attrition and item non-response simultaneously under multiple imputation in a two wave panel with one refreshment sample when the variables involved are categorical and high dimensional. First, I present a semi-parametric selection model that includes an additive non-ignorable attrition model with main effects of all variables, including demographic variables and outcome measures in wave 1 and wave 2. The survey variables are modeled jointly using Bayesian mixture of multinomial distributions. I develop the posterior computation algorithms for the semi-parametric selection model under different prior choices for the regression coefficients in the attrition model. Second, I propose two Bayesian pattern mixture models for this scenario that use latent classes to model the dependency among the variables and the attrition. I develop a dependent Bayesian latent pattern mixture model for which variables are modeled via latent classes and attrition is treated as a covariate in the class allocation weights. And, I develop a joint Bayesian latent pattern mixture model, for which attrition and variables are modeled jointly via latent classes. I show via simulation studies that the pattern mixture models can recover true parameter estimates, even when inferences based on the panel alone are biased from attrition. I apply both the selection and pattern mixture… Advisors/Committee Members: Reiter, Jerome P (advisor).

Subjects/Keywords: Statistics; Categorical; Large scale; Multiple imputation; Nonparametric Bayes; Panel; Refreshment sample

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Si, Y. (2012). Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/5837

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Si, Yajuan. “Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies .” 2012. Thesis, Duke University. Accessed December 08, 2019. http://hdl.handle.net/10161/5837.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Si, Yajuan. “Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies .” 2012. Web. 08 Dec 2019.

Vancouver:

Si Y. Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies . [Internet] [Thesis]. Duke University; 2012. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10161/5837.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Si Y. Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies . [Thesis]. Duke University; 2012. Available from: http://hdl.handle.net/10161/5837

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Open Access Theses and Dissertations