Bayesian analysis for weighted mean-squared error in dual response surface optimization

In Jun Jeong, Kwang Jae Kim, Dennis K.J. Lin

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Dual response surface optimization considers the mean and the variation simultaneously. The minimization of meansquared error (MSE) is an effective approach in dual response surface optimization. Weighted MSE (WMSE) is formed by imposing the relative weights, (λ,1-λ), on the squared bias and variance components of MSE. To date, a few methods have been proposed for determining λ. The resulting λ from these methods is either a single value or an interval. This paper aims at developing a systematic method to choose a λ value when an interval of λ is given. Specifically, this paper proposes a Bayesian approach to construct a probability distribution of λ. Once the distribution of λ is constructed, the expected value of λ can be used to form WMSE.

Original languageEnglish (US)
Pages (from-to)417-430
Number of pages14
JournalQuality and Reliability Engineering International
Volume26
Issue number5
DOIs
StatePublished - Jul 1 2010

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Probability distributions
Mean squared error
Response surface
Bayesian analysis
Relative weight
Bayesian approach
Probability distribution
Expected value
Variance components

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Cite this

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Bayesian analysis for weighted mean-squared error in dual response surface optimization. / Jeong, In Jun; Kim, Kwang Jae; Lin, Dennis K.J.

In: Quality and Reliability Engineering International, Vol. 26, No. 5, 01.07.2010, p. 417-430.

Research output: Contribution to journalArticle

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