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Bayesian Variable Selection for Fractional Factorial Experiments with MultilevelCategorical Factors

Summary: [This abstract is based on the authors' abstract.] The Bayesian approach to fractional factorial experiments is useful for its ability to identify difficult-to-spot interaction effects in experiments with complex aliasing. However, experiments using this approach have consisted either of two-level factors or factors with quantitative levels with priors that cannot be used in experiments with multilevel categorical levels. A simple multivariate normal prior is presented that is a natural extension of the prior used for two-level factors. Its effectiveness is demonstrated by reanalyzing two multilevel factorial experiments with complex aliasing.

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  • Topics: Design of Experiments
  • Keywords: Alias, Complexity, Interactions, Markov chains, Monte Carlo methods, Screening, Prioritization Matrix, Stochastic models
  • Author: Woodward, Phil; Walley, Rosalind
  • Journal: Journal of Quality Technology