Full metadata
Title
Optimal Experimental Designs for Mixed Categorical and Continuous Responses
Description
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 extended to the case of bivariate responses of mixed variable types. Consequently, the number of candidate designs are significantly reduced. An optimization algorithm is then applied to efficiently search the small class of candidate designs for the D- and A-optimal designs. Furthermore, the optimality of the obtained designs is verified by the general equivalence theorem. In the first part of the study, the focus is on a simple, first-order model. The study is expanded to a model with a quadratic polynomial predictor. The obtained designs can help to render a precise statistical inference in practice or serve as a benchmark for evaluating the quality of other designs.
Date Created
2017
Contributors
- Kim, Soohyun (Author)
- Kao, Ming-Hung (Thesis advisor)
- Dueck, Amylou (Committee member)
- Pan, Rong (Committee member)
- Reiser, Mark R. (Committee member)
- Stufken, John (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
122 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.45584
Level of coding
minimal
Note
Doctoral Dissertation Statistics 2017
System Created
- 2017-10-02 07:23:41
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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