Bayesian optimal single-stratum and multistratum designs with high parameter estimation efficiency

Most optimality criteria considered in the literature are model-based criteria that rely on having an assumed model to select an optimal design for the experiment. Having a specified model prior to experimentation might not be feasible in reality. Bayesian optimality criteria has been in the literature for decades to relieve the dependence on an assumed model. In this research, we develop new Bayesian optimality criteria with high parameter estimation efficiency for multistratum designs. Examples with comparisons and sensitivity analyses are provided for selecting optimal designs in completely randomized experiments and multistratum experiments such as split-plot designs using the new criteria.

Author dc:creator

• Miao, Ying

Advisor dc:contributor.supervisor

• Yang, Po

• Bayesian Optimal Design
• Optimal Design

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1993/37611

OAI identifier oai:identifier

oai:mspace.lib.umanitoba.ca:1993/37611