Type of Document Dissertation Author Wu, Sutan Author's Email Address email@example.com URN etd-10272010-194400 Title Goodness-of-fit Tests for Logistic Regression Degree Doctor of Philosophy Department Statistics, Department of Advisory Committee
Advisor Name Title Dan McGee Committee Chair Jinfeng Zhang Committee Co-Chair Debajyoti Sinha Committee Member Myra M. Hurt University Representative Keywords
- Generalized Linear Model
- Stacked Logistic Regression
- Goodness-of-fit Tests
- Logistic Regression
Date of Defense 2010-08-19 Availability unrestricted AbstractThe 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.
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