Title page for ETD etd-122199-103554


Type of Document Dissertation
Author Lin, Hefang
URN etd-122199-103554
Title One-Stage and Bayesian Two-Stage Optimal Designs for Mixture Models
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Myers, Raymond H. Committee Co-Chair
Ye, Keying Committee Co-Chair
Anderson-Cook, Christine M. Committee Member
Foutz, Robert Committee Member
Reynolds, Marion R. Jr. Committee Member
Keywords
  • Optimality
  • Process Variables
  • Mixture Experiments
  • Two-Stage
  • Bayesian
Date of Defense 1999-12-09
Availability unrestricted
Abstract
In this research, Bayesian two-stage D-D optimal designs for mixture experiments with or without process variables under model uncertainty are developed. A Bayesian optimality criterion is used in the first stage to minimize the determinant of the posterior variances of the parameters. The second stage design is then generated according to an optimality procedure that collaborates with the improved model from first stage data. Our results show that the Bayesian two-stage D-D optimal design is more efficient than both the Bayesian one-stage D-optimal design and the non-Bayesian one-stage D-optimal design in most cases. We also use simulations to investigate the ratio between the sample sizes for two stages and to observe least sample size for the first stage. On the other hand, we discuss D-optimal second or higher order designs, and show that Ds-optimal designs are a reasonable alternative to D-optimal designs.
Files
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