{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T19:52:25Z","timestamp":1721245945105},"reference-count":20,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2009,8,10]],"date-time":"2009-08-10T00:00:00Z","timestamp":1249862400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Quality & Reliability Eng"],"published-print":{"date-parts":[[2010,7]]},"abstract":"Abstract<\/jats:title>Dual response surface optimization considers the mean and the variation simultaneously. The minimization of mean\u2010squared error (MSE) is an effective approach in dual response surface optimization. Weighted MSE (WMSE) is formed by imposing the relative weights, (\u03bb, 1\u2212\u03bb), on the squared bias and variance components of MSE. To date, a few methods have been proposed for determining \u03bb. The resulting \u03bb from these methods is either a single value or an interval. This paper aims at developing a systematic method to choose a \u03bb value when an interval of \u03bb is given. Specifically, this paper proposes a Bayesian approach to construct a probability distribution of \u03bb. Once the distribution of \u03bb is constructed, the expected value of \u03bb can be used to form WMSE. 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