An Experimental Design Criterion for Minimizing Meta-Model Prediction Errors Applied to Die Casting Process Design

Abstract

We propose the expected integrated mean-squared error (EIMSE) experimental design criterion and show how we used it to design experiments to meet the needs of researchers in die casting engineering. This criterion expresses in a direct way the researchers' goal to minimize the expected meta-model prediction errors, taking into account the effects of both random experimental errors and errors deriving from our uncertainty about the true model form. Because we needed to make assumptions about the prior distribution of model coefficients to estimate the EIMSE, we performed a sensitivity analysis to verify that the relative prediction performance of the design generated was largely insensitive to our assumptions. Also, we discuss briefly the general advantages of EIMSE optimal designs, including lower expected bias errors compared with popular response surface designs and substantially lower variance errors than certain Box-Draper all-bias designs. Copyright 2003 Royal Statistical Society.

Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science

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