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Optimal Experimental Designs for Mixed Categorical and Continuous Responses


Abstract This study concerns optimal designs for experiments where responses consist of both binary and continuous variables. Many experiments in engineering, medical studies, and other fields have such mixed responses. Although in recent decades several statistical methods have been developed for jointly modeling both types of response variables, an effective way to design such experiments remains unclear. To address this void, some useful results are developed to guide the selection of optimal experimental designs in such studies. The results are mainly built upon a powerful tool called the complete class approach and a nonlinear optimization algorithm. The complete class approach was originally developed for a univariate response, but it is exten... (more)
Created Date 2017
Contributor Kim, Soohyun (Author) / Kao, Ming-Hung (Advisor) / Dueck, Amylou (Committee member) / Pan, Rong (Committee member) / Reiser, Mark (Committee member) / Stufken, John (Committee member) / Arizona State University (Publisher)
Subject Statistics / Binary and continuous responses / Complete class / Generalized linear model / Locally optimal design
Type Doctoral Dissertation
Extent 122 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