Bayesian-inspired minimum aberration two- and four-level designs
成果类型：
Article
署名作者：
Joseph, V. Roshan; Ai, Mingyao; Wu, C. F. Jeff
署名单位：
University System of Georgia; Georgia Institute of Technology; Peking University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asn062
发表日期：
2009
页码：
95106
关键词：
摘要：
Motivated by a Bayesian framework, we propose a new minimum aberration-type criterion for designing experiments with two- and four-level factors. The Bayesian approach helps in overcoming the ad hoc nature of effect ordering in the existing minimum aberration-type criteria. The approach is also capable of distinguishing between qualitative and quantitative factors. Numerous examples are given to demonstrate its advantages.