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Robust surface estimation in multi-response multistage statistical optimization problems

Moslemi, A ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1080/03610918.2017.1291963
  3. Publisher: Taylor and Francis Inc , 2017
  4. Abstract:
  5. As the ordinary least squares (OLS) method is very sensitive to outliers as well as to correlated responses, a robust coefficient estimation method is proposed in this paper for multi-response surfaces in multistage processes based on M-estimators. In this approach, experimental designs are used in which the intermediate response variables may act as covariates in the next stages. The performances of both the ordinary multivariate OLS and the proposed robust multi-response surface approach are analyzed and compared through extensive simulation experiments. Sum of the squared errors in estimating the regression coefficients reveals the efficiency of the proposed robust approach. © 2017 Taylor & Francis Group, LLC
  6. Keywords:
  7. Multi-response surface ; Extensive simulations ; Multistage process ; OLS ; Robust M-estimates ; Correlation methods ; Estimation ; Optimization ; Surface properties ; Correlation ; M-estimates ; Multi-response surfaces ; Ordinary least squares ; Regression coefficient ; Statistical optimization ; Surface estimation ; Least squares approximations
  8. Source: Communications in Statistics: Simulation and Computation ; 2017 , Pages 1-21 ; 03610918 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/03610918.2017.1291963