One-Stage and Bayesian Two-Stage Optimal Designs for Mixture Models

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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Statistics

Department dc:contributor.department

Statistics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

1999

Author dc:creator

• Lin, Hefang

Chairs dc:contributor.committeechair

• Ye, Keying
• Myers, Raymond H.

Committee members dc:contributor.committeemember

• Foutz, Robert
• Anderson-Cook, Christine M.
• Reynolds, Marion R. Jr.

• Optimality
• Process Variables
• Mixture Experiments
• Two-Stage
• Bayesian

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-122199-103554

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/30224