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A Predictive Approach to the Analysis of Designed Experiments
Joseph G. Ibrahim and Purushottam W. Laud
Journal of the American Statistical Association
Vol. 89, No. 425 (Mar., 1994), pp. 309-319
Stable URL: http://www.jstor.org/stable/2291227
Page Count: 11
You can always find the topics here!Topics: Statistical models, Modeling, Statistics, Experiment design, Matrices, Calibration, Mathematical vectors, Predictive modeling, Linear regression, Parametric models
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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 Cp, 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.
Journal of the American Statistical Association © 1994 American Statistical Association