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Variance plus bias optimal response surface designs with qualitative factors applied to stem choice modeling
Author(s) -
Allen Theodore T.,
Tseng ShihHsien
Publication year - 2011
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1210
Subject(s) - context (archaeology) , variance (accounting) , selection (genetic algorithm) , design of experiments , econometrics , selection bias , computer science , optimal design , statistics , mathematics , management science , machine learning , engineering , economics , paleontology , accounting , biology
This paper explores the issue of model misspecification, or bias, in the context of response surface design problems involving quantitative and qualitative factors. New designs are proposed specifically to address bias and compared with five types of alternatives ranging from types of composite to D‐optimal designs using four criteria including D‐efficiency and measured accuracy on test problems. Findings include that certain designs from the literature are expected to cause prediction errors that practitioners would likely find unacceptable. A case study relating to the selection of science, technology, engineering, or mathematics majors by college students confirms that the expected substantial improvements in prediction accuracy using the proposed designs can be realized in relevant situations. Copyright © 2011 John Wiley & Sons, Ltd.