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Bayesian D-Optimal Design Issues and Optimal Design Construction Methods for Generalized Linear Models with Random Blocks

Abstract Optimal experimental design for generalized linear models is often done using a pseudo-Bayesian approach that integrates the design criterion across a prior distribution on the parameter values. This approach ignores the lack of utility of certain models contained in the prior, and a case is demonstrated where the heavy focus on such hopeless models results in a design with poor performance and with wild swings in coverage probabilities for Wald-type confidence intervals. Design construction using a utility-based approach is shown to result in much more stable coverage probabilities in the area of greatest concern.

The pseudo-Bayesian approach can be applied to the problem of optimal design construction under dependent observations. O... (more)
Created Date 2015
Contributor Hassler, Edgar (Author) / Montgomery, Douglas C (Advisor) / Silvestrini, Rachel T (Advisor) / Borror, Connie M (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Subject Statistics / Industrial engineering / Experimental Design / GLM / GLMM / Random Block / Random Intercept
Type Doctoral Dissertation
Extent 113 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Industrial Engineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis