A Bayesian Hierarchical Approach to Dual Response Surface Modeling

TR Number

Date

2005

Journal Title

Journal ISSN

Volume Title

Publisher

Virginia Tech

Abstract

In modern quality engineering, dual response surface methodology is a powerful tool to monitor an industrial process by using both the mean and the standard deviation of the measurements as the responses. The 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 optimal conditions. Based on the inherent hierarchical structure of the dual response problems, we propose a hierarchical Bayesian approach to model dual response surfaces. Such an approach is compared with two frequentist least squares methods by using two real data sets and simulated data.

Description

Keywords

Bayesian hierarchical model, dual response surface, off-line quality control, genetic algorithm, optimization

Citation