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A Bayesian Approach for Model Selection in Fractionated Split Plot Experiments with Applications in Robust Parameter Design

Summary: [This abstract is based on the author's abstract.] This article proposes a Bayesian method for model selection in fractionated split plot experiments. We employ a Bayesian hierarchical model that takes into account the split plot error structure. Expressions for computing the posterior model probability and other important posterior quantities that require evaluation of at most two unidimensional integrals are derived. A novel algorithm called combined global and local search is proposed to find models with high posterior probabilities and to estimate posterior model probabilities. The proposed method is illustrated with the analysis of two real robust design experiments. Simulation studies demonstrate that the method has good performance. Supplementary materials for this article are available online.

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  • Topics: Sampling
  • Keywords: Sampling, Experimental design, Bayesian methods, Split-plot design, Robust design, Posterior, Algorithm
  • Author: Tan, Matthias H. Y.; Wu, C. F. Jeff;
  • Journal: Technometrics