A Bayesian approach to the identification of active location and dispersion factors

ABSTRACT

In this article, we extend the modified Box–Meyer method and propose an approach to identify both active location and dispersion factors in a screening experiment. Since several candidate models can be simultaneously considered under the framework of Bayesian model averaging, the proposed method can overcome the problem of missing the identification of some active factors caused by either the alias structure or misspecification of the location model. For illustration, three practical experiments and one synthetic data set are analyzed.

KEYWORDS:

• Bayesian model averaging
• dispersion effect
• screening experiment

Note on contributor

I-Tang Yu is an Associate Professor in the Department of Statistics, Tunghai University, Taiwan, Republic of China. He received his Ph.D. and M.S. in Statistics from National Chengchi University, Taiwan. His research interests include Bayesian analysis, quality improvement, and reliability.