A confidence region for the ridge path in multiple response surface optimization

Liangxing Shi, Dennis K.J. Lin, John J. Peterson

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Ridge analysis allows the analyst to explore the optimal operating conditions of the experimental factors. A confidence region is desirable for the estimated ridge path. Most literature concentrates on the univariate response situation. Little is known for the confidence region of the ridge path for the multivariate response; only a large-sample confidence interval for the ridge path is available. The simultaneous coverage rate for the existing interval is typically too conservative in practice, especially for small sample sizes. In this paper, the ridge path (via desirability function) is estimated based on the seemingly unrelated regression (SUR) model as well as standard multivariate regression (SMR) model, and a conservative confidence interval suitable for small sample sizes is proposed. It is shown that the proposed method outperforms the existing methods. Real-life examples and simulative study are given for illustration.

Original languageEnglish (US)
Pages (from-to)829-836
Number of pages8
JournalEuropean Journal of Operational Research
Volume252
Issue number3
DOIs
StatePublished - Aug 1 2016

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Multiple Responses
Confidence Region
Response Surface
Ridge
Path
Optimization
Small Sample Size
Confidence interval
Regression Model
Desirability Function
Multivariate Response
Seemingly Unrelated Regression
Multivariate Regression
Multivariate Models
Univariate
Coverage
Response surface
Small sample
Sample size
Confidence region

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

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A confidence region for the ridge path in multiple response surface optimization. / Shi, Liangxing; Lin, Dennis K.J.; Peterson, John J.

In: European Journal of Operational Research, Vol. 252, No. 3, 01.08.2016, p. 829-836.

Research output: Contribution to journalArticle

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