Goodness-of-Tests for Logistic Regression

The generalized linear model and particularly the logistic model are widely used in public health, medicine, and epidemiology. Goodness-of-fit tests for these models are popularly used to describe how well a proposed model fits a set of observations. These different goodness-of-fit tests all have in...

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Other Authors: Wu, Sutan, 1983- (authoraut)
Format: Others
Language:English
English
Published: Florida State University
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Online Access:http://purl.flvc.org/fsu/fd/FSU_migr_etd-0693
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spelling ndltd-fsu.edu-oai-fsu.digital.flvc.org-fsu_2538742020-06-19T03:09:32Z Goodness-of-Tests for Logistic Regression Wu, Sutan, 1983- (authoraut) McGee, Dan L. (professor co-directing dissertation) Zhang, Jinfeng (professor co-directing dissertation) Hurt, Myra (university representative) Sinha, Debajyoti (committee member) Department of Statistics (degree granting department) Florida State University (degree granting institution) Text text Florida State University Florida State University English eng 1 online resource computer application/pdf The generalized linear model and particularly the logistic model are widely used in public health, medicine, and epidemiology. Goodness-of-fit tests for these models are popularly used to describe how well a proposed model fits a set of observations. These different goodness-of-fit tests all have individual advantages and disadvantages. In this thesis, we mainly consider the performance of the "Hosmer-Lemeshow" test, the Pearson's chi-square test, the unweighted sum of squares test and the cumulative residual test. We compare their performance in a series of empirical studies as well as particular simulation scenarios. We conclude that the unweighted sum of squares test and the cumulative sums of residuals test give better overall performance than the other two. We also conclude that the commonly suggested practice of assuming that a p-value less than 0.15 is an indication of lack of fit at the initial steps of model diagnostics should be adopted. Additionally, D'Agostino et al. presented the relationship of the stacked logistic regression and the Cox regression model in the Framingham Heart Study. So in our future study, we will examine the possibility and feasibility of the adaption these goodness-of-fit tests to the Cox proportional hazards model using the stacked logistic regression. A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Fall Semester, 2010. August 19, 2010. Generalized Linear Model, Stacked Logistic Regression, Goodness-of-fit Tests, Logistic Regression Includes bibliographical references. Dan L. McGee, Professor Co-Directing Dissertation; Jinfeng Zhang, Professor Co-Directing Dissertation; Myra Hurt, University Representative; Debajyoti Sinha, Committee Member. Statistics Probabilities FSU_migr_etd-0693 http://purl.flvc.org/fsu/fd/FSU_migr_etd-0693 This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. http://diginole.lib.fsu.edu/islandora/object/fsu%3A253874/datastream/TN/view/Goodness-of-Tests%20for%20Logistic%20Regression.jpg
collection NDLTD
language English
English
format Others
sources NDLTD
topic Statistics
Probabilities
spellingShingle Statistics
Probabilities
Goodness-of-Tests for Logistic Regression
description The generalized linear model and particularly the logistic model are widely used in public health, medicine, and epidemiology. Goodness-of-fit tests for these models are popularly used to describe how well a proposed model fits a set of observations. These different goodness-of-fit tests all have individual advantages and disadvantages. In this thesis, we mainly consider the performance of the "Hosmer-Lemeshow" test, the Pearson's chi-square test, the unweighted sum of squares test and the cumulative residual test. We compare their performance in a series of empirical studies as well as particular simulation scenarios. We conclude that the unweighted sum of squares test and the cumulative sums of residuals test give better overall performance than the other two. We also conclude that the commonly suggested practice of assuming that a p-value less than 0.15 is an indication of lack of fit at the initial steps of model diagnostics should be adopted. Additionally, D'Agostino et al. presented the relationship of the stacked logistic regression and the Cox regression model in the Framingham Heart Study. So in our future study, we will examine the possibility and feasibility of the adaption these goodness-of-fit tests to the Cox proportional hazards model using the stacked logistic regression. === A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Fall Semester, 2010. === August 19, 2010. === Generalized Linear Model, Stacked Logistic Regression, Goodness-of-fit Tests, Logistic Regression === Includes bibliographical references. === Dan L. McGee, Professor Co-Directing Dissertation; Jinfeng Zhang, Professor Co-Directing Dissertation; Myra Hurt, University Representative; Debajyoti Sinha, Committee Member.
author2 Wu, Sutan, 1983- (authoraut)
author_facet Wu, Sutan, 1983- (authoraut)
title Goodness-of-Tests for Logistic Regression
title_short Goodness-of-Tests for Logistic Regression
title_full Goodness-of-Tests for Logistic Regression
title_fullStr Goodness-of-Tests for Logistic Regression
title_full_unstemmed Goodness-of-Tests for Logistic Regression
title_sort goodness-of-tests for logistic regression
publisher Florida State University
url http://purl.flvc.org/fsu/fd/FSU_migr_etd-0693
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