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dc.contributor.advisorWehrly, Thomas E.en_US
dc.creatorChen, Nai-Weien_US
dc.date.accessioned2012-02-14T22:20:16Zen_US
dc.date.accessioned2012-02-16T16:15:35Z
dc.date.available2014-01-15T07:05:28Z
dc.date.created2011-12en_US
dc.date.issued2012-02-14en_US
dc.date.submittedDecember 2011en_US
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10504en_US
dc.description.abstractLinear mixed models and generalized linear mixed models are random-effects models widely applied to analyze clustered or hierarchical data. Generally, random effects are often assumed to be normally distributed in the context of mixed models. However, in the mixed-effects logistic model, the violation of the assumption of normally distributed random effects may result in inconsistency for estimates of some fixed effects and the variance component of random effects when the variance of the random-effects distribution is large. On the other hand, summary statistics used for assessing goodness of fit in the ordinary logistic regression models may not be directly applicable to the mixed-effects logistic models. In this dissertation, we present our investigations of two independent studies related to goodness-of-fit tests in generalized linear mixed models. First, we consider a semi-nonparametric density representation for the random effects distribution and provide a formal statistical test for testing normality of the random-effects distribution in the mixed-effects logistic models. We obtain estimates of parameters by using a non-likelihood-based estimation procedure. Additionally, we not only evaluate the type I error rate of the proposed test statistic through asymptotic results, but also carry out a bootstrap hypothesis testing procedure to control the inflation of the type I error rate and to study the power performance of the proposed test statistic. Further, the methodology is illustrated by revisiting a case study in mental health. Second, to improve assessment of the model fit in the mixed-effects logistic models, we apply the nonparametric local polynomial smoothed residuals over within-cluster continuous covariates to the unweighted sum of squares statistic for assessing the goodness-of-fit of the logistic multilevel models. We perform a simulation study to evaluate the type I error rate and the power performance for detecting a missing quadratic or interaction term of fixed effects using the kernel smoothed unweighted sum of squares statistic based on the local polynomial smoothed residuals over x-space. We also use a real data set in clinical trials to illustrate this application.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.subjectGeneralized linear mixed modelen_US
dc.subjectgeneralized estimating equationsen_US
dc.subjectrobust score testen_US
dc.subjectmisspecification of the random-effect distributionen_US
dc.subjectparametric bootstrap.en_US
dc.titleGoodness-of-Fit Test Issues in Generalized Linear Mixed Modelsen_US
dc.typeThesisen
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.levelDoctoralen_US
dc.contributor.committeeMemberHart, Jeffrey D.en_US
dc.contributor.committeeMemberLord, Dominiqueen_US
dc.contributor.committeeMemberSheather, Simonen_US
dc.type.genrethesisen_US
dc.type.materialtexten_US
local.embargo.terms2014-01-15en_US


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