A Bayesian Model-Averaging Approach for Multiple-Response Optimization

• Autores: Szu Hui NG
• Localización: Journal of quality technology: A quarterly journal of methods applications and related topics, ISSN 0022-4065, Vol. 42, Nº. 1, 2010, págs. 52-68
• Idioma: inglés
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• Resumen

  • Many of the current multiple-response optimization approaches fail to account for uncertainties, resulting in misleading quality estimates that lead to poor product design. This study proposes a Bayesian decision theoretic approach to the modeling and optimization of multiple-response systems that accounts for the correlation among the responses, the variability of the predictions, and the uncertainty of the model parameters. A Bayesian model averaging approach is also proposed to account for response-model uncertainty. The approach is applicable to many types of quality criteria and characteristics.