Title page for ETD etd-12182006-161101

Type of Document Dissertation
Author Zhang, Ying
URN etd-12182006-161101
Title Bayesian D-Optimal Design for Generalized Linear Models
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Ye, Keying Committee Chair
Morgan, John P. Committee Member
Prins, Samantha C. Bates Committee Member
Smith, Eric P. Committee Member
Spitzner, Dan J. Committee Member
  • Design Efficiency
  • Bayesian Optimal Design
  • Genetic Algorithm
  • D-Optimal Design
  • Two-stage Design
Date of Defense 2006-12-07
Availability unrestricted
Bayesian optimal designs have received increasing attention in recent years, especially in biomedical and clinical trials. Bayesian design procedures can utilize the available prior information of the unknown parameters so that a better design can be achieved. However, a difficulty in dealing with the Bayesian design is the lack of efficient computational methods. In this research, a hybrid computational method, which consists of the combination of a rough global optima search and a more precise local optima search, is proposed to efficiently search for the Bayesian D-optimal designs for multi-variable generalized linear models. Particularly, Poisson regression models and logistic regression models are investigated. Designs are examined for a range of prior distributions and the equivalence theorem is used to verify the design optimality. Design efficiency for various models are examined and compared with non-Bayesian designs. Bayesian D-optimal designs are found to be more efficient and robust than non-Bayesian D-optimal designs. Furthermore, the idea of the Bayesian sequential design is introduced and the Bayesian two-stage D-optimal design approach is developed for generalized linear models. With the incorporation of the first stage data information into the second stage, the two-stage design procedure can improve the design efficiency and produce more accurate and robust designs. The Bayesian two-stage D-optimal designs for Poisson and logistic regression models are evaluated based on simulation studies. The Bayesian two-stage optimal design approach is superior to the one-stage approach in terms of a design efficiency criterion.
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