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Three Essays on Comparative Simulation in Three-level Hierarchical Data Structure


Abstract Though the likelihood is a useful tool for obtaining estimates of regression parameters, it is not readily available in the fit of hierarchical binary data models. The correlated observations negate the opportunity to have a joint likelihood when fitting hierarchical logistic regression models. Through conditional likelihood, inferences for the regression and covariance parameters as well as the intraclass correlation coefficients are usually obtained. In those cases, I have resorted to use of Laplace approximation and large sample theory approach for point and interval estimates such as Wald-type confidence intervals and profile likelihood confidence intervals. These methods rely on distributional assumptions and large sample theory. Howev... (more)
Created Date 2017
Contributor Wang, Bei (Author) / Wilson, Jeffrey R (Advisor) / Kamarianakis, Ioannis (Committee member) / Reiser, Mark (Committee member) / St Louis, Robert (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Subject Statistics / Binary response / Bootstrapping / Generalized linear mixed model / Hierarchical data / Rresampling schemes / Small sample inferences
Type Doctoral Dissertation
Extent 112 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Statistics 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis