Bayesian analysis for weighted mean‐squared error in dual response surface optimization |
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Authors: | In‐Jun Jeong Kwang‐Jae Kim Dennis K. J. Lin |
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Affiliation: | 1. Technology Business Division, Electronics and Telecommunications Research Institute, Daejeon 305‐700, Republic of Korea;2. Division of Mechanical and Industrial Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk 790‐784, Republic of Korea;3. Department of Statistics, The Pennsylvania State University, University Park, PA 16802, U.S.A. |
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Abstract: | Dual response surface optimization considers the mean and the variation simultaneously. The minimization of mean‐squared 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. Copyright © 2009 John Wiley & Sons, Ltd. |
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Keywords: | Bayesian analysis interval of weight weighted mean‐squared error |
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