JSM 2004 - Toronto

Abstract #301900

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Activity Number: 331
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Quality and Productivity
Abstract - #301900
Title: A Bayesian Approach to Dual Response Surface Modeling
Author(s): Younan Chen*+ and Keying Ye
Companies: Virginia Polytechnic Institute and State University and Virginia Polytechnic Institute and State University
Address: 406 A Hutcheson Hall, Blacksburg, VA, 24061,
Keywords: dual response surface ; Bayesian method
Abstract:

In modern quality engineering, dual response surface methodology is a powerful tool to monitor an industrial process by using both the mean and standard deviation of the measurements as the responses. Least squares method in regression is often used to estimate the coefficients in the mean and standard deviation models, and various decision criteria are proposed by researchers to find the optimum conditions. We discuss a Bayesian approach to investigate dual response surface modeling. The comparisons of the results derived from both Bayesian and non-Bayesian regressions are made by using simulation data and a real dataset.

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