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A Bayesian Approach to the Analysis of Split-Plot Product Arrays and Optimization in Robust Parameter Design

Published

Author(s)

Adam L. Pintar

Abstract

Many robust parameter design (RPD) studies involve a split-plot randomization structure and to obtain valid inferences in the analysis, it is essential to account for the design induced correlation structure. Bayesian methods are appealing for these studies since they naturally accommodate a general class of models, can account for parameter uncertainty in process optimization, and offer the necessary flexibility when one is interested in non-standard performance criteria. In this article, we present a Bayesian approach to process optimization for a general class of RPD models in the split-plot context using an empirical approximation to the posterior distribution of an objective function of interest. Two examples from the literature are used for illustration.
Citation
Journal of Quality Technology

Keywords

Bayesian Predictive Density, Generalized Linear Mixed Models, Hard-to-Change Factor, Markov Chain Monte Carlo, Process Optimization, Response Surface, Restricted Randomization.

Citation

Pintar, A. (2012), A Bayesian Approach to the Analysis of Split-Plot Product Arrays and Optimization in Robust Parameter Design, Journal of Quality Technology, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=907885 (Accessed July 15, 2024)

Issues

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Created October 4, 2012, Updated January 27, 2020