Optimal weighting of bias and variance in dual response surface optimization

Abstract

Dual response surface optimization simultaneously considers the mean and the standard deviation of a response. The minimization of the mean squared error (MSE) is a simple, yet effective, approach in dual response surface optimization. The bias and variance components of MSE need to be weighted properly if they are not of the same importance in the given problem situation. To date, the relative weights of bias and variance have been equally set or determined only by the data. However, the weights should be determined in accordance with the tradeoffs on various factors in quality and costs. In this paper, we propose a systematic method to determine the weights of bias and variance in accordance with a decision maker's preference structure regarding the tradeoffs.