A Predictive Approach to the Analysis of Designed Experiments

Joseph G. Ibrahim · Purushottam W. Laud

96 citations0/0 files storedJournal of the American Statistical Association

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Optimal Experimental Design Methods Statistical Methods and Bayesian Inference Statistical Methods and Inference

Abstract

Viewing the analysis of designed experiments as a model selection problem, we introduce the use of a predictive Bayesian criterion in this context based on the predictive density of a replicate experiment (PDRE). A calibration of the criterion is provided to assist in the model choice. The relationships of the proposed criterion to other prevalent criteria, such as AIC, BIC, and Mallows's C p , are given. An information theoretic criterion based on the PDRE's of two competing models is also introduced and compared with the usual F statistic for two nested models. Examples are given to illustrate the proposed methodology.

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2007Design and Analysis of Experiments 1999Statistical Design and Analysis of Experiments 1972Statistical Design and Analysis of Experiments

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2001Accounting for input model and parameter uncertainty in simulation

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