subject:(logistic regression)

University of Pretoria

1. [No author]. The variable selection problem and the application of the roc curve for binary outcome variables .

Degree: 2008, University of Pretoria

URL: http://upetd.up.ac.za/thesis/available/etd-08112008-104847/

► Variable selection refers to the problem of selecting input variables that are most predictive of a given outcome. Variable selection problems are found in all… (more)

▼ Variable selection refers to the problem of selecting input variables that are most predictive of a given outcome. Variable selection problems are found in all machine learning tasks, supervised or unsupervised, classification, regression, time series prediction , two - class or multi-class, posing various levels of challenges. Variables selection problems are related to the problems of input dimensionality reduction and of parameter planning. It has practical and theoretical challenges of its own. From the practical point of view, eliminating variables may reduce the cost of producing the outcome and increase its speed, while space dimensionality does not address these problems. Theoretical challenges include estimating with what confidence one can state that a variable is relevant to the concept when it is useful to the outcome and providing a theoretical understanding of the stability of selected variables subsets. As the probability cut-points increase in value, the more likely it becomes that an observation is classified as a non-event by the selected variables. The mathematical statement of the problem is not widely agreed upon and may depend on the application. One typically distinguishes: i) The problem of discovering all the variables relevant to the outcome variable and determine HOW relevant they are and how they are related to each other. ii) The problem of finding a minimum subset of variables that is useful to the outcome variable. Logistic regression is an increasingly popular statistical technique used to model the probability of discrete binary outcome. Logistic regression applies maximum likelihood estimation after transforming the outcome variable into a logit variable. In this way, logistic regression estimates the probability of a certain event. When properly applied, logistic regression analyses yield a very powerful insight in to what variables are more or less likely to predict event outcome in a population of interest. These models also show the extent to which changes in the values of the variable may increase or decrease the predicted probability of event outcome. Variable selection, in all its facets is similarly important with logistic regression. The receiver operating characteristics (ROC) curve is a graphic display that gives a measure of the predictive accuracy of a logistic regression model. It is a measure of classification performance, the area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. Another measure of predictive accuracy of a logistic regression model is a classification table. It uses the model to classifying observations as events if their estimated probability is greater or equal to a given probability cut-point, otherwise events are classified as non-events. This technique, as it appears in the literature, is also studied in this thesis. In this thesis the issue of variable selection, both for continuous and binary outcome variables, is investigated as it appears in the statistical literature. It is clear that this topic has been widely… Advisors/Committee Members: Prof H T Groeneveld (advisor), Prof A J van der Merwe (advisor).

Subjects/Keywords: Logistic regression; Parameter planning; UCTD

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

APA (6^th Edition):

author], [. (2008). The variable selection problem and the application of the roc curve for binary outcome variables . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-08112008-104847/

Chicago Manual of Style (16^th Edition):

author], [No. “The variable selection problem and the application of the roc curve for binary outcome variables .” 2008. Masters Thesis, University of Pretoria. Accessed March 01, 2021. http://upetd.up.ac.za/thesis/available/etd-08112008-104847/.

MLA Handbook (7^th Edition):

author], [No. “The variable selection problem and the application of the roc curve for binary outcome variables .” 2008. Web. 01 Mar 2021.

Vancouver:

author] [. The variable selection problem and the application of the roc curve for binary outcome variables . [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2021 Mar 01]. Available from: http://upetd.up.ac.za/thesis/available/etd-08112008-104847/.

Council of Science Editors:

author] [. The variable selection problem and the application of the roc curve for binary outcome variables . [Masters Thesis]. University of Pretoria; 2008. Available from: http://upetd.up.ac.za/thesis/available/etd-08112008-104847/

Humboldt State University

2. Barnes, Jason M. A sociospatial model to predict the success of entrepreneurs in Mendocino County, California.

Degree: MS, Natural Resources: Natural Resources Planning and Interpretation, 2012, Humboldt State University

URL: http://hdl.handle.net/2148/1257

► Predicting business success in rural areas solely on spatial characteristics is virtually nonexistent in literature. In this study I used logistic regression, geographical information systems… (more)

Subjects/Keywords: Sociospatial; GIS; Prediction; Logistic regression

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

APA (6^th Edition):

Barnes, J. M. (2012). A sociospatial model to predict the success of entrepreneurs in Mendocino County, California. (Masters Thesis). Humboldt State University. Retrieved from http://hdl.handle.net/2148/1257

Chicago Manual of Style (16^th Edition):

Barnes, Jason M. “A sociospatial model to predict the success of entrepreneurs in Mendocino County, California.” 2012. Masters Thesis, Humboldt State University. Accessed March 01, 2021. http://hdl.handle.net/2148/1257.

MLA Handbook (7^th Edition):

Barnes, Jason M. “A sociospatial model to predict the success of entrepreneurs in Mendocino County, California.” 2012. Web. 01 Mar 2021.

Vancouver:

Barnes JM. A sociospatial model to predict the success of entrepreneurs in Mendocino County, California. [Internet] [Masters thesis]. Humboldt State University; 2012. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/2148/1257.

Council of Science Editors:

Barnes JM. A sociospatial model to predict the success of entrepreneurs in Mendocino County, California. [Masters Thesis]. Humboldt State University; 2012. Available from: http://hdl.handle.net/2148/1257

Mississippi State University

3. Smith, Sara Ann. GIS-based evaluation of landslide susceptibility for Eastern Tennessee.

Degree: MS, Geosciences, 2017, Mississippi State University

URL: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02232017-132221/ ;

► The Appalachian Mountains in eastern Tennessee are known for landslides, and landslides are reported to cause millions of dollars of damage. To aid in… (more)

Subjects/Keywords: logistic regression; Tennessee; GIS; landslides

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APA (6^th Edition):

Smith, S. A. (2017). GIS-based evaluation of landslide susceptibility for Eastern Tennessee. (Masters Thesis). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-02232017-132221/ ;

Chicago Manual of Style (16^th Edition):

Smith, Sara Ann. “GIS-based evaluation of landslide susceptibility for Eastern Tennessee.” 2017. Masters Thesis, Mississippi State University. Accessed March 01, 2021. http://sun.library.msstate.edu/ETD-db/theses/available/etd-02232017-132221/ ;.

MLA Handbook (7^th Edition):

Smith, Sara Ann. “GIS-based evaluation of landslide susceptibility for Eastern Tennessee.” 2017. Web. 01 Mar 2021.

Vancouver:

Smith SA. GIS-based evaluation of landslide susceptibility for Eastern Tennessee. [Internet] [Masters thesis]. Mississippi State University; 2017. [cited 2021 Mar 01]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02232017-132221/ ;.

Council of Science Editors:

Smith SA. GIS-based evaluation of landslide susceptibility for Eastern Tennessee. [Masters Thesis]. Mississippi State University; 2017. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02232017-132221/ ;

University of Pretoria

4. Matshego, James Moeng. The variable selection problem and the application of the roc curve for binary outcome variables.

Degree: Statistics, 2008, University of Pretoria

URL: http://hdl.handle.net/2263/27133

► Variable selection refers to the problem of selecting input variables that are most predictive of a given outcome. Variable selection problems are found in all… (more)

▼ Variable selection refers to the problem of selecting input variables that are most predictive of a given outcome. Variable selection problems are found in all machine learning tasks, supervised or unsupervised, classification, regression, time series prediction , two - class or multi-class, posing various levels of challenges. Variables selection problems are related to the problems of input dimensionality reduction and of parameter planning. It has practical and theoretical challenges of its own. From the practical point of view, eliminating variables may reduce the cost of producing the outcome and increase its speed, while space dimensionality does not address these problems. Theoretical challenges include estimating with what confidence one can state that a variable is relevant to the concept when it is useful to the outcome and providing a theoretical understanding of the stability of selected variables subsets. As the probability cut-points increase in value, the more likely it becomes that an observation is classified as a non-event by the selected variables. The mathematical statement of the problem is not widely agreed upon and may depend on the application. One typically distinguishes: i) The problem of discovering all the variables relevant to the outcome variable and determine HOW relevant they are and how they are related to each other. ii) The problem of finding a minimum subset of variables that is useful to the outcome variable. Logistic regression is an increasingly popular statistical technique used to model the probability of discrete binary outcome. Logistic regression applies maximum likelihood estimation after transforming the outcome variable into a logit variable. In this way, logistic regression estimates the probability of a certain event. When properly applied, logistic regression analyses yield a very powerful insight in to what variables are more or less likely to predict event outcome in a population of interest. These models also show the extent to which changes in the values of the variable may increase or decrease the predicted probability of event outcome. Variable selection, in all its facets is similarly important with logistic regression. The receiver operating characteristics (ROC) curve is a graphic display that gives a measure of the predictive accuracy of a logistic regression model. It is a measure of classification performance, the area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. Another measure of predictive accuracy of a logistic regression model is a classification table. It uses the model to classifying observations as events if their estimated probability is greater or equal to a given probability cut-point, otherwise events are classified as non-events. This technique, as it appears in the literature, is also studied in this thesis. In this thesis the issue of variable selection, both for continuous and binary outcome variables, is investigated as it appears in the statistical literature. It is clear that this topic has been widely… Advisors/Committee Members: Prof H T Groeneveld (advisor), Prof A J van der Merwe (advisor).

Subjects/Keywords: Logistic regression; Parameter planning; UCTD

Record Details Similar Records Cite « Share

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

APA (6^th Edition):

Matshego, J. (2008). The variable selection problem and the application of the roc curve for binary outcome variables. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/27133

Chicago Manual of Style (16^th Edition):

Matshego, James. “The variable selection problem and the application of the roc curve for binary outcome variables.” 2008. Masters Thesis, University of Pretoria. Accessed March 01, 2021. http://hdl.handle.net/2263/27133.

MLA Handbook (7^th Edition):

Matshego, James. “The variable selection problem and the application of the roc curve for binary outcome variables.” 2008. Web. 01 Mar 2021.

Vancouver:

Matshego J. The variable selection problem and the application of the roc curve for binary outcome variables. [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/2263/27133.

Council of Science Editors:

Matshego J. The variable selection problem and the application of the roc curve for binary outcome variables. [Masters Thesis]. University of Pretoria; 2008. Available from: http://hdl.handle.net/2263/27133

Vanderbilt University

5. Stanley, Brooklyn. Outcome Misclassification in Logistic Regression: Examining Hospitalization Risk and its Association with Health Literacy.

Degree: MS, Biostatistics, 2019, Vanderbilt University

URL: http://hdl.handle.net/1803/10805

► In a cohort of patients seeking primary care, the Mid-South Coronary Heart Disease Cohort Study (MSCHDCS) sought to examine the association between health literacy and… (more)

Subjects/Keywords: misclassification; hospitalization; logistic regression

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APA (6^th Edition):

Stanley, B. (2019). Outcome Misclassification in Logistic Regression: Examining Hospitalization Risk and its Association with Health Literacy. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/10805

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):

Stanley, Brooklyn. “Outcome Misclassification in Logistic Regression: Examining Hospitalization Risk and its Association with Health Literacy.” 2019. Thesis, Vanderbilt University. Accessed March 01, 2021. http://hdl.handle.net/1803/10805.

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

MLA Handbook (7^th Edition):

Stanley, Brooklyn. “Outcome Misclassification in Logistic Regression: Examining Hospitalization Risk and its Association with Health Literacy.” 2019. Web. 01 Mar 2021.

Vancouver:

Stanley B. Outcome Misclassification in Logistic Regression: Examining Hospitalization Risk and its Association with Health Literacy. [Internet] [Thesis]. Vanderbilt University; 2019. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/1803/10805.

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

Council of Science Editors:

Stanley B. Outcome Misclassification in Logistic Regression: Examining Hospitalization Risk and its Association with Health Literacy. [Thesis]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/10805

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

Kwame Nkrumah University of Science and Technology

6. Mensah, George Yaw. Determination of Some Factors that Influences Loan Default Payment: Case Study: Customers from Akatakyiman Rural Bank Ltd Komenda.

Degree: 2012, Kwame Nkrumah University of Science and Technology

URL: http://dspace.knust.edu.gh:8080/jspui/handle/123456789/4091

►

Loan Default is the failure of an applicant to fulfil his/her obligation with respect to repayment of loans. Loan default lowest the financial capacity of… (more)

Subjects/Keywords: Loan default; Logistic regression; Risk

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APA (6^th Edition):

Mensah, G. Y. (2012). Determination of Some Factors that Influences Loan Default Payment: Case Study: Customers from Akatakyiman Rural Bank Ltd Komenda. (Thesis). Kwame Nkrumah University of Science and Technology. Retrieved from http://dspace.knust.edu.gh:8080/jspui/handle/123456789/4091

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):

Mensah, George Yaw. “Determination of Some Factors that Influences Loan Default Payment: Case Study: Customers from Akatakyiman Rural Bank Ltd Komenda.” 2012. Thesis, Kwame Nkrumah University of Science and Technology. Accessed March 01, 2021. http://dspace.knust.edu.gh:8080/jspui/handle/123456789/4091.

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

MLA Handbook (7^th Edition):

Mensah, George Yaw. “Determination of Some Factors that Influences Loan Default Payment: Case Study: Customers from Akatakyiman Rural Bank Ltd Komenda.” 2012. Web. 01 Mar 2021.

Vancouver:

Mensah GY. Determination of Some Factors that Influences Loan Default Payment: Case Study: Customers from Akatakyiman Rural Bank Ltd Komenda. [Internet] [Thesis]. Kwame Nkrumah University of Science and Technology; 2012. [cited 2021 Mar 01]. Available from: http://dspace.knust.edu.gh:8080/jspui/handle/123456789/4091.

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

Council of Science Editors:

Mensah GY. Determination of Some Factors that Influences Loan Default Payment: Case Study: Customers from Akatakyiman Rural Bank Ltd Komenda. [Thesis]. Kwame Nkrumah University of Science and Technology; 2012. Available from: http://dspace.knust.edu.gh:8080/jspui/handle/123456789/4091

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

7. Kwémou Djoukoué, Marius. Réduction de dimension en régression logistique, application aux données actu-palu : Dimension reduction in logistic regression, application to actu-palu data.

Degree: Docteur es, Mathématiques appliquées, 2014, Evry-Val d'Essonne; Université de Saint-Louis (Sénégal)

URL: http://www.theses.fr/2014EVRY0030

►

Cette thèse est consacrée à la sélection de variables ou de modèles en régression logistique. Elle peut-être divisée en deux parties, une partie appliquée et… (more)

▼

Cette thèse est consacrée à la sélection de variables ou de modèles en régression logistique. Elle peut-être divisée en deux parties, une partie appliquée et une partie méthodologique. La partie appliquée porte sur l'analyse des données d'une grande enquête socio - épidémiologique dénommée actu-palu. Ces grandes enquêtes socio - épidémiologiques impliquent généralement un nombre considérable de variables explicatives. Le contexte est par nature dit de grande dimension. En raison du fléau de la dimension, le modèle de régression logistique n'est pas directement applicable. Nous procédons en deux étapes, une première étape de réduction du nombre de variables par les méthodes Lasso, Group Lasso et les forêts aléatoires. La deuxième étape consiste à appliquer le modèle logistique au sous-ensemble de variables sélectionné à la première étape. Ces méthodes ont permis de sélectionner les variables pertinentes pour l'identification des foyers à risque d'avoir un épisode fébrile chez un enfant de 2 à 10 ans à Dakar. La partie méthodologique, composée de deux sous-parties, porte sur l'établissement de propriétés techniques d'estimateurs dans le modèle de régression logistique non paramétrique. Ces estimateurs sont obtenus par maximum de vraisemblance pénalisé, dans un cas avec une pénalité de type Lasso ou Group Lasso et dans l'autre cas avec une pénalité de type 1 exposant 0. Dans un premier temps, nous proposons des versions pondérées des estimateurs Lasso et Group Lasso pour le modèle logistique non paramétrique. Nous établissons des inégalités oracles non asymptotiques pour ces estimateurs. Un deuxième ensemble de résultats vise à étendre le principe de sélection de modèle introduit par Birgé et Massart (2001) à la régression logistique. Cette sélection se fait via des critères du maximum de vraisemblance pénalisé. Nous proposons dans ce contexte des critères de sélection de modèle, et nous établissons des inégalités oracles non asymptotiques pour les estimateurs sélectionnés. La pénalité utilisée, dépendant uniquement des données, est calibrée suivant l'idée de l'heuristique de pente. Tous les résultats de la partie méthodologique sont illustrés par des études de simulations numériques.

This thesis is devoted to variables selection or model selection in logistic regression. The applied part focuses on the analysis of data from a large socioepidémiological survey, called actu-palu. These large socioepidemiological survey typically involve a considerable number of explanatory variables. This is well-known as high-dimensional setting. Due to the curse of dimensionality, logistic regression model is no longer reliable. We proceed in two steps, a first step of reducing the number of variables by the Lasso, Group Lasso ans random forests methods. The second step is to apply the logistic model to the sub-set of variables selected in the first step. These methods have helped to select relevant variables for the identification of households at risk of having febrile episode amongst children from 2 to 10 years old in Dakar. In…

Advisors/Committee Members: Taupin, Marie-Luce (thesis director), Diongue, Abdou Kâ (thesis director).

Subjects/Keywords: Régression logistique; Logistic regression

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

APA (6^th Edition):

Kwémou Djoukoué, M. (2014). Réduction de dimension en régression logistique, application aux données actu-palu : Dimension reduction in logistic regression, application to actu-palu data. (Doctoral Dissertation). Evry-Val d'Essonne; Université de Saint-Louis (Sénégal). Retrieved from http://www.theses.fr/2014EVRY0030

Chicago Manual of Style (16^th Edition):

Kwémou Djoukoué, Marius. “Réduction de dimension en régression logistique, application aux données actu-palu : Dimension reduction in logistic regression, application to actu-palu data.” 2014. Doctoral Dissertation, Evry-Val d'Essonne; Université de Saint-Louis (Sénégal). Accessed March 01, 2021. http://www.theses.fr/2014EVRY0030.

MLA Handbook (7^th Edition):

Kwémou Djoukoué, Marius. “Réduction de dimension en régression logistique, application aux données actu-palu : Dimension reduction in logistic regression, application to actu-palu data.” 2014. Web. 01 Mar 2021.

Vancouver:

Kwémou Djoukoué M. Réduction de dimension en régression logistique, application aux données actu-palu : Dimension reduction in logistic regression, application to actu-palu data. [Internet] [Doctoral dissertation]. Evry-Val d'Essonne; Université de Saint-Louis (Sénégal); 2014. [cited 2021 Mar 01]. Available from: http://www.theses.fr/2014EVRY0030.

Council of Science Editors:

Kwémou Djoukoué M. Réduction de dimension en régression logistique, application aux données actu-palu : Dimension reduction in logistic regression, application to actu-palu data. [Doctoral Dissertation]. Evry-Val d'Essonne; Université de Saint-Louis (Sénégal); 2014. Available from: http://www.theses.fr/2014EVRY0030

University of Manitoba

8. Shen, Yi (Jack). A multinomial logistic regression analysis of youth gambling in Manitoba using Cycle 2 of the Manitoba Longitudinal Study of Young Adults.

Degree: Sociology, 2018, University of Manitoba

URL: http://hdl.handle.net/1993/32795

► Problem gambling is still a relatively new form of addiction in terms of the amount of research that has been put into it. The data… (more)

Subjects/Keywords: Problem gambling; Logistic regression

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

APA (6^th Edition):

Shen, Y. (. (2018). A multinomial logistic regression analysis of youth gambling in Manitoba using Cycle 2 of the Manitoba Longitudinal Study of Young Adults. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/32795

Chicago Manual of Style (16^th Edition):

Shen, Yi (Jack). “A multinomial logistic regression analysis of youth gambling in Manitoba using Cycle 2 of the Manitoba Longitudinal Study of Young Adults.” 2018. Masters Thesis, University of Manitoba. Accessed March 01, 2021. http://hdl.handle.net/1993/32795.

MLA Handbook (7^th Edition):

Shen, Yi (Jack). “A multinomial logistic regression analysis of youth gambling in Manitoba using Cycle 2 of the Manitoba Longitudinal Study of Young Adults.” 2018. Web. 01 Mar 2021.

Vancouver:

Shen Y(. A multinomial logistic regression analysis of youth gambling in Manitoba using Cycle 2 of the Manitoba Longitudinal Study of Young Adults. [Internet] [Masters thesis]. University of Manitoba; 2018. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/1993/32795.

Council of Science Editors:

Shen Y(. A multinomial logistic regression analysis of youth gambling in Manitoba using Cycle 2 of the Manitoba Longitudinal Study of Young Adults. [Masters Thesis]. University of Manitoba; 2018. Available from: http://hdl.handle.net/1993/32795

Colorado School of Mines

9. Cherry, Lisa. Predicting parcel scale redevelopment within the Berkeley neighborhood in Denver, Colorado using linear and logistic regression.

Degree: MS(M.S.), Civil and Environmental Engineering, 2016, Colorado School of Mines

URL: http://hdl.handle.net/11124/170658

► Many water resource issues associated with urban development result from increased impervious cover. As impervious cover increases, rainwater infiltration decreases leading to increased flows and… (more)

▼ Many water resource issues associated with urban development result from increased impervious cover. As impervious cover increases, rainwater infiltration decreases leading to increased flows and potentially higher pollutant loads in the runoff. Most of the prior research on this topic investigates the increase of impervious cover through the transformation of undeveloped to developed regions, or the expansion of dense urban development into outlying suburban areas. A topic that is not as widely understood is the impact of infill redevelopment on stormwater runoff. Infill redevelopment is rapidly occurring in many Denver neighborhoods, where previously developed properties with low-density structures are being replaced by larger, higher density units. Regardless of impervious cover increase, these redevelopment projects are only required to incorporate stormwater detention and treatment systems if they are greater than one acre. Due to most of the redevelopment in Denver (86%) occurring on sites less than one acre, the burden of stormwater treatment and detention ultimately falls on the city. This study focuses on modeling the spatial distribution of infill re-development on a parcel scale to investigate its cumulative impacts on stormwater quality and quantity for near-term and future conditions. Future redevelopment and imperviousness is determined by distributing a “business as usual” linear growth scenario to the parcels with the greatest probability of future redevelopment. Then, a logistic regression model is used to determine the parcels that will be redeveloped. Results indicate that building cover change within study site from 2004 – 2014 followed a linear pattern. During this period the total building cover increased by 17% or 1.7 % per year on average. The logistic regression model determined the total value, year built, percent difference between current and max building cover, the current use classification: rowhomes, and current use classification apartments to be the greatest predictors of redevelopment, resulting in a model that was 81 % accurate. The "Building to Land Area Ratio" variable was found to be highly correlated with the “Improvement to Land Value Ratio”. However, the “Building to Land Area Ratio” was found to be a better predictor of redevelopment. The final model estimated an increase of 820,498 sq. ft. (18.8 acres) in building coverage between 2014 and 2024. This method will provide municipalities with a tool that can be used to estimate parcel scale impervious cover growth from publicly available planning data resulting in more informed urban watershed planning and policy development. Advisors/Committee Members: McCray, John E. (advisor), Hogue, Terri S. (committee member), Eisenstein, William (committee member).

Subjects/Keywords: logistic regression; redevelopment; parcel; infill

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

APA (6^th Edition):

Cherry, L. (2016). Predicting parcel scale redevelopment within the Berkeley neighborhood in Denver, Colorado using linear and logistic regression. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/170658

Chicago Manual of Style (16^th Edition):

Cherry, Lisa. “Predicting parcel scale redevelopment within the Berkeley neighborhood in Denver, Colorado using linear and logistic regression.” 2016. Masters Thesis, Colorado School of Mines. Accessed March 01, 2021. http://hdl.handle.net/11124/170658.

MLA Handbook (7^th Edition):

Cherry, Lisa. “Predicting parcel scale redevelopment within the Berkeley neighborhood in Denver, Colorado using linear and logistic regression.” 2016. Web. 01 Mar 2021.

Vancouver:

Cherry L. Predicting parcel scale redevelopment within the Berkeley neighborhood in Denver, Colorado using linear and logistic regression. [Internet] [Masters thesis]. Colorado School of Mines; 2016. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/11124/170658.

Council of Science Editors:

Cherry L. Predicting parcel scale redevelopment within the Berkeley neighborhood in Denver, Colorado using linear and logistic regression. [Masters Thesis]. Colorado School of Mines; 2016. Available from: http://hdl.handle.net/11124/170658

Duquesne University

10. Nelson, Bryan. Modeling the NCAA Tournament Through Bayesian Logistic Regression.

Degree: MS, Computational Mathematics, 2012, Duquesne University

URL: https://dsc.duq.edu/etd/970

► Many rating systems exist that order the Division I teams in Men's College Basketball that compete in the NCAA Tournament, such as seeding teams on… (more)

Subjects/Keywords: Bayesian; Logistic regression; MCMC; NCAA

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

APA (6^th Edition):

Nelson, B. (2012). Modeling the NCAA Tournament Through Bayesian Logistic Regression. (Masters Thesis). Duquesne University. Retrieved from https://dsc.duq.edu/etd/970

Chicago Manual of Style (16^th Edition):

Nelson, Bryan. “Modeling the NCAA Tournament Through Bayesian Logistic Regression.” 2012. Masters Thesis, Duquesne University. Accessed March 01, 2021. https://dsc.duq.edu/etd/970.

MLA Handbook (7^th Edition):

Nelson, Bryan. “Modeling the NCAA Tournament Through Bayesian Logistic Regression.” 2012. Web. 01 Mar 2021.

Vancouver:

Nelson B. Modeling the NCAA Tournament Through Bayesian Logistic Regression. [Internet] [Masters thesis]. Duquesne University; 2012. [cited 2021 Mar 01]. Available from: https://dsc.duq.edu/etd/970.

Council of Science Editors:

Nelson B. Modeling the NCAA Tournament Through Bayesian Logistic Regression. [Masters Thesis]. Duquesne University; 2012. Available from: https://dsc.duq.edu/etd/970

University of Georgia

11. Tan, Xiaoyan. Analysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models.

Degree: 2018, University of Georgia

URL: http://hdl.handle.net/10724/38087

► Considering traditional animal-based testing approaches are expensive and time-consuming, the application of in vitro testing to predict patterns of toxicity was highlighted, ToxCast program was… (more)

Subjects/Keywords: ToxCast; SVM; logistic regression; CART

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APA (6^th Edition):

Tan, X. (2018). Analysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/38087

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):

Tan, Xiaoyan. “Analysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models.” 2018. Thesis, University of Georgia. Accessed March 01, 2021. http://hdl.handle.net/10724/38087.

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

MLA Handbook (7^th Edition):

Tan, Xiaoyan. “Analysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models.” 2018. Web. 01 Mar 2021.

Vancouver:

Tan X. Analysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10724/38087.

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

Council of Science Editors:

Tan X. Analysis of toxcast phase ii data-predicting in vivo toxicities from in vitro data using optimal models. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/38087

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

12. Ishikawa, Noemi Ichihara. Uso de transformações em modelos de regressão logística.

Degree: Mestrado, Estatística, 2007, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05062007-202656/ ;

►

Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo… (more)

Subjects/Keywords: regression logistic regression models; transformações regressão logística

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APA (6^th Edition):

Ishikawa, N. I. (2007). Uso de transformações em modelos de regressão logística. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05062007-202656/ ;

Chicago Manual of Style (16^th Edition):

Ishikawa, Noemi Ichihara. “Uso de transformações em modelos de regressão logística.” 2007. Masters Thesis, University of São Paulo. Accessed March 01, 2021. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05062007-202656/ ;.

MLA Handbook (7^th Edition):

Ishikawa, Noemi Ichihara. “Uso de transformações em modelos de regressão logística.” 2007. Web. 01 Mar 2021.

Vancouver:

Ishikawa NI. Uso de transformações em modelos de regressão logística. [Internet] [Masters thesis]. University of São Paulo; 2007. [cited 2021 Mar 01]. Available from: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05062007-202656/ ;.

Council of Science Editors:

Ishikawa NI. Uso de transformações em modelos de regressão logística. [Masters Thesis]. University of São Paulo; 2007. Available from: http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05062007-202656/ ;

Florida International University

13. Huang, Lixin. Predicting Hurricane Evacuation Decisions: When, How Many, and How Far.

Degree: Civil Engineering, 2011, Florida International University

URL: https://digitalcommons.fiu.edu/etd/461 ; 10.25148/etd.FI11081005 ; FI11081005

► Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is… (more)

▼ Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation. Advisors/Committee Members: Albert Gan, L. David Shen, Mohammed Hadi, Zhenmin Chen.

Subjects/Keywords: Hurricane Evacuation; Logistic Regression; Geographically Weighted Regression

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APA (6^th Edition):

Huang, L. (2011). Predicting Hurricane Evacuation Decisions: When, How Many, and How Far. (Thesis). Florida International University. Retrieved from https://digitalcommons.fiu.edu/etd/461 ; 10.25148/etd.FI11081005 ; FI11081005

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):

Huang, Lixin. “Predicting Hurricane Evacuation Decisions: When, How Many, and How Far.” 2011. Thesis, Florida International University. Accessed March 01, 2021. https://digitalcommons.fiu.edu/etd/461 ; 10.25148/etd.FI11081005 ; FI11081005.

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

MLA Handbook (7^th Edition):

Huang, Lixin. “Predicting Hurricane Evacuation Decisions: When, How Many, and How Far.” 2011. Web. 01 Mar 2021.

Vancouver:

Huang L. Predicting Hurricane Evacuation Decisions: When, How Many, and How Far. [Internet] [Thesis]. Florida International University; 2011. [cited 2021 Mar 01]. Available from: https://digitalcommons.fiu.edu/etd/461 ; 10.25148/etd.FI11081005 ; FI11081005.

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

Council of Science Editors:

Huang L. Predicting Hurricane Evacuation Decisions: When, How Many, and How Far. [Thesis]. Florida International University; 2011. Available from: https://digitalcommons.fiu.edu/etd/461 ; 10.25148/etd.FI11081005 ; FI11081005

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

University of North Texas

14. Weng, Yu. Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models.

Degree: 2013, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc407796/

► We consider the problem of maximum likelihood estimation of logistic sinusoidal regression models and develop some asymptotic theory including the consistency and joint rates of… (more)

Subjects/Keywords: Maximum likelihood; estimation; logistic regression; sinusoidal regression

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APA (6^th Edition):

Weng, Y. (2013). Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc407796/

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):

Weng, Yu. “Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models.” 2013. Thesis, University of North Texas. Accessed March 01, 2021. https://digital.library.unt.edu/ark:/67531/metadc407796/.

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

MLA Handbook (7^th Edition):

Weng, Yu. “Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models.” 2013. Web. 01 Mar 2021.

Vancouver:

Weng Y. Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models. [Internet] [Thesis]. University of North Texas; 2013. [cited 2021 Mar 01]. Available from: https://digital.library.unt.edu/ark:/67531/metadc407796/.

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

Council of Science Editors:

Weng Y. Maximum Likelihood Estimation of Logistic Sinusoidal Regression Models. [Thesis]. University of North Texas; 2013. Available from: https://digital.library.unt.edu/ark:/67531/metadc407796/

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

University of South Africa

15. Muchabaiwa, Honest. Logistic regression to determine significant factors associated with share price change .

Degree: 2013, University of South Africa

URL: http://hdl.handle.net/10500/13229

► This thesis investigates the factors that are associated with annual changes in the share price of Johannesburg Stock Exchange (JSE) listed companies. In this study,… (more)

Subjects/Keywords: Logistic regression; Binary logistic regression; Share price; Stock exchange; Akaike’s Information Criterion; Wald Test; Score test; Enter method; Stepwise logistic regression

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APA (6^th Edition):

Muchabaiwa, H. (2013). Logistic regression to determine significant factors associated with share price change . (Masters Thesis). University of South Africa. Retrieved from http://hdl.handle.net/10500/13229

Chicago Manual of Style (16^th Edition):

Muchabaiwa, Honest. “Logistic regression to determine significant factors associated with share price change .” 2013. Masters Thesis, University of South Africa. Accessed March 01, 2021. http://hdl.handle.net/10500/13229.

MLA Handbook (7^th Edition):

Muchabaiwa, Honest. “Logistic regression to determine significant factors associated with share price change .” 2013. Web. 01 Mar 2021.

Vancouver:

Muchabaiwa H. Logistic regression to determine significant factors associated with share price change . [Internet] [Masters thesis]. University of South Africa; 2013. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10500/13229.

Council of Science Editors:

Muchabaiwa H. Logistic regression to determine significant factors associated with share price change . [Masters Thesis]. University of South Africa; 2013. Available from: http://hdl.handle.net/10500/13229

NSYSU

16. Shieh, Feng-Cheng. Using CRM Data for Customer Loyalty Prediction â A Case Study in Electrical & Electronic Industry.

Degree: Master, Information Management, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0105114-121324

► Due to the advances in information and internet technology, customers nowadays can easily acquire information about prices and specifications of desired products. Thus, how to… (more)

Subjects/Keywords: Customer Relationship Management; Logistic Regression; Brand; Loyalty

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APA (6^th Edition):

Shieh, F. (2014). Using CRM Data for Customer Loyalty Prediction â A Case Study in Electrical & Electronic Industry. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0105114-121324

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):

Shieh, Feng-Cheng. “Using CRM Data for Customer Loyalty Prediction â A Case Study in Electrical & Electronic Industry.” 2014. Thesis, NSYSU. Accessed March 01, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0105114-121324.

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

MLA Handbook (7^th Edition):

Shieh, Feng-Cheng. “Using CRM Data for Customer Loyalty Prediction â A Case Study in Electrical & Electronic Industry.” 2014. Web. 01 Mar 2021.

Vancouver:

Shieh F. Using CRM Data for Customer Loyalty Prediction â A Case Study in Electrical & Electronic Industry. [Internet] [Thesis]. NSYSU; 2014. [cited 2021 Mar 01]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0105114-121324.

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

Council of Science Editors:

Shieh F. Using CRM Data for Customer Loyalty Prediction â A Case Study in Electrical & Electronic Industry. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0105114-121324

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

NSYSU

17. Lin, Shui-Pa. An Empirical Analysis of The Effect of Cram Education on Civil Servantâs Human Capital.

Degree: Master, EMPP, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0611114-211030

► âCram schoolâ, a common phenomenon in Taiwan, steady became a kind of trend now. Numbers of cram schools grew up dramatically in the past decade.… (more)

Subjects/Keywords: Logistic Regression; Cram school; Officersâ Test

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APA (6^th Edition):

Lin, S. (2014). An Empirical Analysis of The Effect of Cram Education on Civil Servantâs Human Capital. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0611114-211030

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):

Lin, Shui-Pa. “An Empirical Analysis of The Effect of Cram Education on Civil Servantâs Human Capital.” 2014. Thesis, NSYSU. Accessed March 01, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0611114-211030.

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

MLA Handbook (7^th Edition):

Lin, Shui-Pa. “An Empirical Analysis of The Effect of Cram Education on Civil Servantâs Human Capital.” 2014. Web. 01 Mar 2021.

Vancouver:

Lin S. An Empirical Analysis of The Effect of Cram Education on Civil Servantâs Human Capital. [Internet] [Thesis]. NSYSU; 2014. [cited 2021 Mar 01]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0611114-211030.

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

Council of Science Editors:

Lin S. An Empirical Analysis of The Effect of Cram Education on Civil Servantâs Human Capital. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0611114-211030

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

NSYSU

18. Chao, Tung-bo. Analysis of Unexpected Readmission of Elderly Pneumonia Patient.

Degree: Master, Institute of Health Care Management, 2012, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0626112-185914

► Objectives: This Study wanted to analysis the characteristics of the elder adult who had hospitalized with pneumonia. We also evaluated the factors that will affect… (more)

▼ Objectives: This Study wanted to analysis the characteristics of the elder adult who had hospitalized with pneumonia. We also evaluated the factors that will affect the unexpected readmission in elderly pneumonia patients. Methods: This is a retrospective cohort study design. The study data was collected 341 pneumonia patients who have hospitalized in a general teaching hospital in Kaohsiung city from year 2009 to 2010. The study population was divided into two groups, the sample size of the old group (age >= 65yrs), and the young group (age < 65yrs) was 173 and 168, respectively. The methods of stepwise multiple logistic regressions were needed to evaluate the association between aging and different days of unplanned readmission in adult pneumonia patients. Results: All the 341 adult pneumonia patients, we found 613 male and 926 female. The demography characteristic of the study subjects, the means of age was 61.9yrs (s.d. = 19.3yrs), and BMI was 23.4 kg/m2 (s.d. = 4.5 kg/m2). The percentage of ICD-9-CM that code 486 was 95.6%. Most patients were community-acquired pneumonia (98.8%), hospitalized from emergency room (85.3%), and admission in general wards (92.7%). The unplanned readmission within 14/30 days, 60 days, and 90days were 9.1%, 11.7%, and 15.0%, respectively. The significant factors that were associated with readmission within 14 days include age, Hb, hospitalized days, hypertension, and other disease. When we used the multiple logistic regression analysis to adjust the other variables, only age still significant with readmission within 14 days (the crude OR of the old group was 4.561, adjusted OR was 2.714, 95% CI of OR from 1.002 to 7.353). In the stepwise multiple logistic regression models, the variable that was associated with readmission with 14 or 30 days were age (>= 65yrs, OR = 3.025), WBC (>=10750 mm3, OR=2.917), and Hb (>=12.4 g/dL, OR=0.390). We remain the elderly subjects to evaluate the factor that will influence readmission states. In all the stepwise logistic regression models, we found the experience with used endotracheal tube in the hospitalized period were the significant increases the readmission rate within 14 or 30 days, 60 days, and 90 days. Conclusion: In our study shows that the situations of unexpected readmission in pneumonia patients were strong association with aging. We suggest that the indicator of medical quality should be adjusted before we comparison the readmission rate in the different institute. The major factors that will be associated to affect the readmission states were endotracheal tube used (significant with 14 or 30 days readmission rate), CRP level (significant with 60 days and 90 days readmission rate), and Hb level (significant with 60 days and 90 days readmission rate). Advisors/Committee Members: Chen-guo Ker (chair), Ying-chun Li (committee member), Yuan-yi Chia (chair).

Subjects/Keywords: unexpected readmission; pneumonia; logistic regression analysis; elderly

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APA (6^th Edition):

Chao, T. (2012). Analysis of Unexpected Readmission of Elderly Pneumonia Patient. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0626112-185914

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):

Chao, Tung-bo. “Analysis of Unexpected Readmission of Elderly Pneumonia Patient.” 2012. Thesis, NSYSU. Accessed March 01, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0626112-185914.

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

MLA Handbook (7^th Edition):

Chao, Tung-bo. “Analysis of Unexpected Readmission of Elderly Pneumonia Patient.” 2012. Web. 01 Mar 2021.

Vancouver:

Chao T. Analysis of Unexpected Readmission of Elderly Pneumonia Patient. [Internet] [Thesis]. NSYSU; 2012. [cited 2021 Mar 01]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0626112-185914.

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

Council of Science Editors:

Chao T. Analysis of Unexpected Readmission of Elderly Pneumonia Patient. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0626112-185914

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

San Jose State University

19. Strauss, Benjamin Nathan. Predicting Switch-like Behavior in Proteins using Logistic Regression on Sequence-based Descriptors.

Degree: MS, Computer Science, 2019, San Jose State University

URL: https://scholarworks.sjsu.edu/etd_theses/5082

► Ligands can bind at specific protein locations, inducing conformational changes such as those involving secondary structure. Identifying these possible switches from sequence, including homology,… (more)

Subjects/Keywords: logistic regression; protein switch; qualitative predictors

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APA (6^th Edition):

Strauss, B. N. (2019). Predicting Switch-like Behavior in Proteins using Logistic Regression on Sequence-based Descriptors. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_theses/5082

Chicago Manual of Style (16^th Edition):

Strauss, Benjamin Nathan. “Predicting Switch-like Behavior in Proteins using Logistic Regression on Sequence-based Descriptors.” 2019. Masters Thesis, San Jose State University. Accessed March 01, 2021. https://scholarworks.sjsu.edu/etd_theses/5082.

MLA Handbook (7^th Edition):

Strauss, Benjamin Nathan. “Predicting Switch-like Behavior in Proteins using Logistic Regression on Sequence-based Descriptors.” 2019. Web. 01 Mar 2021.

Vancouver:

Strauss BN. Predicting Switch-like Behavior in Proteins using Logistic Regression on Sequence-based Descriptors. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Mar 01]. Available from: https://scholarworks.sjsu.edu/etd_theses/5082.

Council of Science Editors:

Strauss BN. Predicting Switch-like Behavior in Proteins using Logistic Regression on Sequence-based Descriptors. [Masters Thesis]. San Jose State University; 2019. Available from: https://scholarworks.sjsu.edu/etd_theses/5082

Vanderbilt University

20. Michels, Kara Ann. Uterine Fibroid Severity in Pregnancy.

Degree: PhD, Epidemiology, 2014, Vanderbilt University

URL: http://hdl.handle.net/1803/13832

► Uterine fibroids are benign tumors associated with an increased risk of irregular vaginal bleeding and abdominal pain. Disease severity can be described in the context… (more)

Subjects/Keywords: leiomyoma; multilevel; obstetrics; polytomous logistic regression; southeast

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APA (6^th Edition):

Michels, K. A. (2014). Uterine Fibroid Severity in Pregnancy. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13832

Chicago Manual of Style (16^th Edition):

Michels, Kara Ann. “Uterine Fibroid Severity in Pregnancy.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed March 01, 2021. http://hdl.handle.net/1803/13832.

MLA Handbook (7^th Edition):

Michels, Kara Ann. “Uterine Fibroid Severity in Pregnancy.” 2014. Web. 01 Mar 2021.

Vancouver:

Michels KA. Uterine Fibroid Severity in Pregnancy. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/1803/13832.

Council of Science Editors:

Michels KA. Uterine Fibroid Severity in Pregnancy. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/13832

Texas A&M University

21. Miao, Jingang. New Advances in Logistic Regression for Handling Missing and Mismeasured Data with Applications in Biostatistics.

Degree: PhD, Statistics, 2014, Texas A&M University

URL: http://hdl.handle.net/1969.1/153244

► As a probabilistic statistical classification model, logistic regression (or logit regression) is widely used to model the outcome of a categorical dependent variable based on… (more)

▼ As a probabilistic statistical classification model, logistic regression (or logit regression) is widely used to model the outcome of a categorical dependent variable based on one or more predictor variables/features. We study two problems related to logistic regression with applications in biostatistics. In the first problem, we study multivariate disease classification in the presence of partially missing disease traits. In modern cancer epidemiology, diseases are classified based on pathologic and molecular traits, and different combinations of these traits give rise to many disease subtypes. The effect of predictor variables can be measured by fitting a polytomous logistic model to such data. The differences (heterogeneity) among the relative risk parameters associated with subtypes are of great interest to better understand disease etiology. Due to the heterogeneity of the relative risk parameters, when a risk factor is changed, the prevalence of one subtype may change more than that of another subtype does. Estimation of the heterogeneity parameters is difficult when disease trait information is only partially observed and the number of disease subtypes is large. We consider a robust semiparametric approach based on the pseudo conditional likelihood for estimating these heterogeneity parameters. Through simulation studies, we compare the robustness and efficiency of our approach with the maximum likelihood approach. The method is then applied to analyze data from the American Cancer Society Cancer Prevention Study (CPS) II Nutrition Cohort. Weight gain was associated with the risk of breast cancer and the association varies by disease subtype. In the second problem, we use a semiparametric Bayesian method to handle measurement errors. In nutritional epidemiological studies, nutrient intakes are often measured via food frequency questionnaires and 24-hour dietary recalls. Due to self reporting, recall error, and other reasons, the measured nutrient intakes can involve a substantial amount of noise. While independence assumption between the measurement error and the true predictor is likely to be a reasonable assumption for the main effect of the predictors, this assumption is not tenable for the interaction effect of two predictors measured with error. Although there are a number of flexible methods for handling additive, homogeneous measurement error in predictors in logistic regression models, relatively less attention has been paid to handling measurement error that depends on the unobserved predictor. Therefore, we propose a semiparametric Bayesian method for handling this unorthodox measurement error scenario in logistic regression models in the presence of the interaction term. The proposed method is also designed to handle partially missing values for the error-prone surrogate variables. Through simulation studies, we assess some operating characteristics of the proposed method and compare it with the simulation extrapolation and the regression calibration method. Our method has smaller biases than the… Advisors/Committee Members: Wang, Suojin (advisor), Sinha, Samiran (advisor), Longnecker, Michael (committee member), McKyer, E. Lisako J (committee member).

Subjects/Keywords: Logistic regression; Measurement error; Disease classification

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APA (6^th Edition):

Miao, J. (2014). New Advances in Logistic Regression for Handling Missing and Mismeasured Data with Applications in Biostatistics. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153244

Chicago Manual of Style (16^th Edition):

Miao, Jingang. “New Advances in Logistic Regression for Handling Missing and Mismeasured Data with Applications in Biostatistics.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 01, 2021. http://hdl.handle.net/1969.1/153244.

MLA Handbook (7^th Edition):

Miao, Jingang. “New Advances in Logistic Regression for Handling Missing and Mismeasured Data with Applications in Biostatistics.” 2014. Web. 01 Mar 2021.

Vancouver:

Miao J. New Advances in Logistic Regression for Handling Missing and Mismeasured Data with Applications in Biostatistics. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/1969.1/153244.

Council of Science Editors:

Miao J. New Advances in Logistic Regression for Handling Missing and Mismeasured Data with Applications in Biostatistics. [Doctoral Dissertation]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153244

University of KwaZulu-Natal

22. Dlamini, Silungile Mukelwa. Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa.

Degree: 2019, University of KwaZulu-Natal

URL: https://researchspace.ukzn.ac.za/handle/10413/17321

► The planning and management of forest operations is a complex task. This complexity is attributed to the variability of forest plantations’ site and topographic conditions.… (more)

▼ The planning and management of forest operations is a complex task. This complexity is attributed to the variability of forest plantations’ site and topographic conditions. Therefore, there is a need for an integrated approach towards forest management and decision-making which offer a continuous review of forest operation systems used to increase site productivity. Therefore, the aim of this study was to determine the impact of local terrain on forest operations and forest productivity using GIS and statistical modelling in a commercial forest plantation in KwaZulu-Natal, South Africa. The first objective of the study focused on determining the influence of terrain variation on the productivity of commercial forestry using LiDAR-derived topographic factors. A 1m LiDAR-derived Canopy Height Model (CHM) and 30m digital elevation model (DEM) were used in the study. Four model scenarios were generated using LiDAR data in conjunction with a stepwise multiple linear regression. The results showed that elevation, aspect, clay content and slope were significant variables in influencing tree productivity (R2 > 0.9). Furthermore, a strong correlation was observed between observed tree heights and predicted tree height values (R2 = 0.88). The results of the study suggest that topographic variables strongly influence commercial tree species productivity. The second objective of the study determined optimal terrain classes based on a national terrain classification system developed by Erasmus (1994) for South African forestry regions. The integration of logistic regression in a GIS environment proved to accurately map suitable terrain classes (AUC = 0.93). This was achieved using a cost-effective 30 m DEM and SOTER-based soil parameter estimates (SOTWIS) data which was used to derive site topographic variables. The study demonstrates the use of integrated approaches for providing efficient and feasible means to apply terrain classification for current forestry practices. The study provides an effective framework for classifying ideal terrain conditions for forest management applications and forest operations. Overall, the study establishes the significance of local topography on commercial forest production and contributes towards enhancing management decision-making during spatial planning initiatives and operations. Advisors/Committee Members: Mutanga, Onisimo. (advisor), Kabir, Peerbhay. (advisor).

Subjects/Keywords: Topography.; Forestry.; Terrain classification.; Logistic regression.

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APA (6^th Edition):

Dlamini, S. M. (2019). Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa. (Thesis). University of KwaZulu-Natal. Retrieved from https://researchspace.ukzn.ac.za/handle/10413/17321

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):

Dlamini, Silungile Mukelwa. “Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa.” 2019. Thesis, University of KwaZulu-Natal. Accessed March 01, 2021. https://researchspace.ukzn.ac.za/handle/10413/17321.

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

MLA Handbook (7^th Edition):

Dlamini, Silungile Mukelwa. “Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa.” 2019. Web. 01 Mar 2021.

Vancouver:

Dlamini SM. Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa. [Internet] [Thesis]. University of KwaZulu-Natal; 2019. [cited 2021 Mar 01]. Available from: https://researchspace.ukzn.ac.za/handle/10413/17321.

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

Council of Science Editors:

Dlamini SM. Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa. [Thesis]. University of KwaZulu-Natal; 2019. Available from: https://researchspace.ukzn.ac.za/handle/10413/17321

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

University of Nairobi

23. Adede, Chrisgone O. Model for predicting the probability of event occurrence using logistic regression: case for a Kenyan commercial bank .

Degree: 2012, University of Nairobi

URL: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/10144

► Credit Scoring has been a key undertaking of lenders over the last few decades. However, the most common use of credit scoring has been as… (more)

Subjects/Keywords: probability; logistic regression; Kenyan commercial bank

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APA (6^th Edition):

Adede, C. O. (2012). Model for predicting the probability of event occurrence using logistic regression: case for a Kenyan commercial bank . (Thesis). University of Nairobi. Retrieved from http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/10144

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):

Adede, Chrisgone O. “Model for predicting the probability of event occurrence using logistic regression: case for a Kenyan commercial bank .” 2012. Thesis, University of Nairobi. Accessed March 01, 2021. http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/10144.

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

MLA Handbook (7^th Edition):

Adede, Chrisgone O. “Model for predicting the probability of event occurrence using logistic regression: case for a Kenyan commercial bank .” 2012. Web. 01 Mar 2021.

Vancouver:

Adede CO. Model for predicting the probability of event occurrence using logistic regression: case for a Kenyan commercial bank . [Internet] [Thesis]. University of Nairobi; 2012. [cited 2021 Mar 01]. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/10144.

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

Council of Science Editors:

Adede CO. Model for predicting the probability of event occurrence using logistic regression: case for a Kenyan commercial bank . [Thesis]. University of Nairobi; 2012. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/10144

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

University of Nairobi

24. Nzioki, Nicholas M. Use Of Logistic Regression Model To Determine The Key Contaminants Of Water In Yatta District .

Degree: 2012, University of Nairobi

URL: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/11957

► Over one sixth of the world's population lacks safe drinking-water sources. The water crisis in Kenya is due not only to the wave of droughts… (more)

Subjects/Keywords: Logistic regression; Contaminants of water; Yatta District

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APA (6^th Edition):

Nzioki, N. M. (2012). Use Of Logistic Regression Model To Determine The Key Contaminants Of Water In Yatta District . (Thesis). University of Nairobi. Retrieved from http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/11957

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):

Nzioki, Nicholas M. “Use Of Logistic Regression Model To Determine The Key Contaminants Of Water In Yatta District .” 2012. Thesis, University of Nairobi. Accessed March 01, 2021. http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/11957.

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

MLA Handbook (7^th Edition):

Nzioki, Nicholas M. “Use Of Logistic Regression Model To Determine The Key Contaminants Of Water In Yatta District .” 2012. Web. 01 Mar 2021.

Vancouver:

Nzioki NM. Use Of Logistic Regression Model To Determine The Key Contaminants Of Water In Yatta District . [Internet] [Thesis]. University of Nairobi; 2012. [cited 2021 Mar 01]. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/11957.

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

Council of Science Editors:

Nzioki NM. Use Of Logistic Regression Model To Determine The Key Contaminants Of Water In Yatta District . [Thesis]. University of Nairobi; 2012. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/11957

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

University of Nairobi

25. Obayo, Eunice. Evaluation of vct utilization by women in Kenya using the logistic regression model .

Degree: 2010, University of Nairobi

URL: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/14223

► HIV/AIDS pandemic is a global crisis with consequences that will be felt for decades to come. It has already claimed more than 25 million lives… (more)

Subjects/Keywords: Evaluation; Vct utilization; Women; Logistic regression model

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APA (6^th Edition):

Obayo, E. (2010). Evaluation of vct utilization by women in Kenya using the logistic regression model . (Thesis). University of Nairobi. Retrieved from http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/14223

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):

Obayo, Eunice. “Evaluation of vct utilization by women in Kenya using the logistic regression model .” 2010. Thesis, University of Nairobi. Accessed March 01, 2021. http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/14223.

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

MLA Handbook (7^th Edition):

Obayo, Eunice. “Evaluation of vct utilization by women in Kenya using the logistic regression model .” 2010. Web. 01 Mar 2021.

Vancouver:

Obayo E. Evaluation of vct utilization by women in Kenya using the logistic regression model . [Internet] [Thesis]. University of Nairobi; 2010. [cited 2021 Mar 01]. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/14223.

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

Council of Science Editors:

Obayo E. Evaluation of vct utilization by women in Kenya using the logistic regression model . [Thesis]. University of Nairobi; 2010. Available from: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/14223

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

University of Waikato

26. Forsythe, Claire Elizabeth. Predictive Risk Modelling for Hospital Readmissions .

Degree: 2014, University of Waikato

URL: http://hdl.handle.net/10289/8699

► This thesis is concerned with developing a predictive risk model to identify patients that are at high risk of readmission to hospital. Such a model… (more)

▼ This thesis is concerned with developing a predictive risk model to identify patients that are at high risk of readmission to hospital. Such a model should have a desirable level of predictive accuracy but also, should be financially beneficial to the DHB. Logistic regression and Naive Bayes probabilistic classification methods were both considered to build the predictive model. Performance measures such as the positive predictive value (PPV) and cost savings analysis were used to find the optimal days between initial admission and readmission and the optimal threshold for prediction of high risk patients. This study is concerned with Waikato District Health Board (DHB) domiciled patients discharged between 1 July 2009 and 31 October 2013. The dataset includes information about the patients initial admission and the response variable is whether a readmission occurred or not. Using logistic regression, this study found the model that fits the data best includes 21 variables that contain information about the patients initial admission. The two classification methods used produce a risk probability between 0 and 1 for each patient in the study. The logistic regression model performance was better than Naive Bayes as shown by the PPV (the proportion of patients correctly identified as at risk over the total at risk). The 56 day readmission data PPV at a risk threshold of 0.5 for the logistic regression was 48.4% and 30.8% for Naive Bayes. Analysis of the PPV identifies the risk threshold level of 0.5 and readmission period of 56 days as optimal predictive criteria in this study. Cost savings analysis also supports the 56 day model with an intervention cost of $500. The 0.5 cut off point in the 56 day model identifies a reasonable number of patients at risk for intervention, approximately 3,000, which equates to about 2 patients at risk per day over the period of this analysis. This analysis found the optimal model for predicting patients at risk of readmission is a logistic regression model using 56 day readmission data and a risk threshold of 0.5. A key recommendation of this study is that the DHB needs to introduce a method that correctly flags patient admissions. The model should be used on a trial basis at the DHB to see how accurate it performs. Advisors/Committee Members: Joshi, Chaitanya (advisor).

Subjects/Keywords: Logistic regression; Readmissions; Predictive model; Hospital

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

APA (6^th Edition):

Forsythe, C. E. (2014). Predictive Risk Modelling for Hospital Readmissions . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/8699

Chicago Manual of Style (16^th Edition):

Forsythe, Claire Elizabeth. “Predictive Risk Modelling for Hospital Readmissions .” 2014. Masters Thesis, University of Waikato. Accessed March 01, 2021. http://hdl.handle.net/10289/8699.

MLA Handbook (7^th Edition):

Forsythe, Claire Elizabeth. “Predictive Risk Modelling for Hospital Readmissions .” 2014. Web. 01 Mar 2021.

Vancouver:

Forsythe CE. Predictive Risk Modelling for Hospital Readmissions . [Internet] [Masters thesis]. University of Waikato; 2014. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10289/8699.

Council of Science Editors:

Forsythe CE. Predictive Risk Modelling for Hospital Readmissions . [Masters Thesis]. University of Waikato; 2014. Available from: http://hdl.handle.net/10289/8699

University of Otago

27. Buchanan, Robert. Numerical methods for bankruptcy prediction: A comparison between data envelopment analysis and logistic regression .

Degree: 2011, University of Otago

URL: http://hdl.handle.net/10523/1373

► This research examines the efficacy of Data Envelopment Analysis (DEA) as a model for bankruptcy prediction compared to the logistic regression method. A sample of… (more)

Subjects/Keywords: Data Envelopment Analysis; bankruptcy prediction; logistic regression

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APA (6^th Edition):

Buchanan, R. (2011). Numerical methods for bankruptcy prediction: A comparison between data envelopment analysis and logistic regression . (Masters Thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/1373

Chicago Manual of Style (16^th Edition):

Buchanan, Robert. “Numerical methods for bankruptcy prediction: A comparison between data envelopment analysis and logistic regression .” 2011. Masters Thesis, University of Otago. Accessed March 01, 2021. http://hdl.handle.net/10523/1373.

MLA Handbook (7^th Edition):

Buchanan, Robert. “Numerical methods for bankruptcy prediction: A comparison between data envelopment analysis and logistic regression .” 2011. Web. 01 Mar 2021.

Vancouver:

Buchanan R. Numerical methods for bankruptcy prediction: A comparison between data envelopment analysis and logistic regression . [Internet] [Masters thesis]. University of Otago; 2011. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10523/1373.

Council of Science Editors:

Buchanan R. Numerical methods for bankruptcy prediction: A comparison between data envelopment analysis and logistic regression . [Masters Thesis]. University of Otago; 2011. Available from: http://hdl.handle.net/10523/1373

Colorado State University

28. Slade, Stephanie A. Statistical prediction model for East Pacific and Atlantic tropical cyclone genesis, A.

Degree: MS(M.S.), Atmospheric Science, 2012, Colorado State University

URL: http://hdl.handle.net/10217/66684

► A statistical model is developed via multiple logistic regression for the prediction of weekly tropical cyclone activity over the East Pacific and Atlantic Ocean regions… (more)

Subjects/Keywords: Atlantic; hurricanes; logistic; Pacific; prediction; regression

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APA (6^th Edition):

Slade, S. A. (2012). Statistical prediction model for East Pacific and Atlantic tropical cyclone genesis, A. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/66684

Chicago Manual of Style (16^th Edition):

Slade, Stephanie A. “Statistical prediction model for East Pacific and Atlantic tropical cyclone genesis, A.” 2012. Masters Thesis, Colorado State University. Accessed March 01, 2021. http://hdl.handle.net/10217/66684.

MLA Handbook (7^th Edition):

Slade, Stephanie A. “Statistical prediction model for East Pacific and Atlantic tropical cyclone genesis, A.” 2012. Web. 01 Mar 2021.

Vancouver:

Slade SA. Statistical prediction model for East Pacific and Atlantic tropical cyclone genesis, A. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10217/66684.

Council of Science Editors:

Slade SA. Statistical prediction model for East Pacific and Atlantic tropical cyclone genesis, A. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/66684

29. Liao, Xiaolu. Export Propensity of Canadian SMEs: A Gender Based Study .

Degree: 2015, University of Ottawa

URL: http://hdl.handle.net/10393/32506

► SME exporters constitute a critical economic force that contributes significantly to national productivity and job creation in the Canadian economy. However, the academic literature suggests… (more)

Subjects/Keywords: Export; SMEs; Female; Gender; Logistic Regression; Interaction

5 more images…

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APA (6^th Edition):

Liao, X. (2015). Export Propensity of Canadian SMEs: A Gender Based Study . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/32506

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):

Liao, Xiaolu. “Export Propensity of Canadian SMEs: A Gender Based Study .” 2015. Thesis, University of Ottawa. Accessed March 01, 2021. http://hdl.handle.net/10393/32506.

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

MLA Handbook (7^th Edition):

Liao, Xiaolu. “Export Propensity of Canadian SMEs: A Gender Based Study .” 2015. Web. 01 Mar 2021.

Vancouver:

Liao X. Export Propensity of Canadian SMEs: A Gender Based Study . [Internet] [Thesis]. University of Ottawa; 2015. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/10393/32506.

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

Council of Science Editors:

Liao X. Export Propensity of Canadian SMEs: A Gender Based Study . [Thesis]. University of Ottawa; 2015. Available from: http://hdl.handle.net/10393/32506

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

Baylor University

30. Guo, Yuanyuan, 1984-. Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.

Degree: PhD, Baylor University. Dept. of Statistical Sciences., 2015, Baylor University

URL: http://hdl.handle.net/2104/9557

► Conventional Phase II clinical trial designs usually employ a logistic regression model to analyze the efficacy of a new drug and, therefore, assumes monotone dose-response… (more)

▼ Conventional Phase II clinical trial designs usually employ a logistic regression model to analyze the efficacy of a new drug and, therefore, assumes monotone dose-response relationship. Also, the logistic regression model requires the response to be categorical and, thus, it is not applicable for continuous data. The traditional design in Phase II determines if a new drug will be further tested in Phase III based on only drug efficacy and allocates an equal number of patients to each dosage, ignoring dose efficacy. Because of the limitations of conventional clinical trial designs, new adaptive designs have been proposed by researchers to improve the flexibility and adaptability of conventional designs. In Chapter Two we propose an adaptive Bayesian design that uses a bivariate normal dynamic linear model for a Phase II clinical trial, and we compare its performance to a Bayesian fixed or non-adaptive design. The proposed Bayesian adaptive design can be utilized for continuous data and can model various dose-response relationships. We remark that for many dose-response relationships, our proposed adaptive Bayesian design can use fewer patients to obtain a correct decision concerning a drug's efficacy than the Bayesian fixed design. Missing data arises in almost all research; that is, part of the data are missing for a subject. A data analyst must decide how to cope with the missing data from among the numerous imputation methods that can be used. However, one might not know which imputation method is the best. The objective of this study is to evaluate the efficacy of five imputation methods. In Chapter Four, we have compared the performance of complete-data-only, single-mean imputation, conditional-mean imputation, multiple imputation by chained equations and hotdeck imputation methods for prediction of a logistic regression model, for the missing-completely-at-random and missing-at-random mechanisms. These five imputation methods yield different results for small sample sizes, and the difference decreases with an increasing sample size. Surprisingly, a single-mean imputation method performs as well as the multiple imputation methods compared here. Advisors/Committee Members: Young, Dean M. (advisor).

Subjects/Keywords: Clinical trial. Dynamic linear model. Logistic regression.

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

APA (6^th Edition):

Guo, Yuanyuan, 1. (2015). Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression. (Doctoral Dissertation). Baylor University. Retrieved from http://hdl.handle.net/2104/9557

Chicago Manual of Style (16^th Edition):

Guo, Yuanyuan, 1984-. “Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.” 2015. Doctoral Dissertation, Baylor University. Accessed March 01, 2021. http://hdl.handle.net/2104/9557.

MLA Handbook (7^th Edition):

Guo, Yuanyuan, 1984-. “Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.” 2015. Web. 01 Mar 2021.

Vancouver:

Guo, Yuanyuan 1. Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression. [Internet] [Doctoral dissertation]. Baylor University; 2015. [cited 2021 Mar 01]. Available from: http://hdl.handle.net/2104/9557.

Council of Science Editors:

Guo, Yuanyuan 1. Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression. [Doctoral Dissertation]. Baylor University; 2015. Available from: http://hdl.handle.net/2104/9557

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Open Access Theses and Dissertations