Experimental Designs for Estimating Both Mean and Variance Functions

Article

Vining, G. Geoffrey; Schaub, Diane   (1996, ASQC)   University of Florida, Gainesville, FL

Journal of Quality Technology    Vol. 28    No. 2
QICID: 11444    April 1996    pp. 135-147
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Article Abstract

Statisticians are increasingly finding applications which require separate linear models for a response of interest and this response's variance. A crucial question then becomes what are reasonable experimental strategies which will allow the estimation of both of these functions. This paper pursues two distinct approaches: a one-step approach which, in the absence of any information about the process variance, initially assumes that the process variance is constant over the region of interest; and a one-step, semi-Bayesian approach which attempts to develop an appropriate experimental plan in light of prior information about the nature of the variance function. These two approaches are compared in a simulation study to illuminate their relative advantages and disadvantages. An example illustrates the proposed methodology.

Keywords

D-optimality,Taguchi method,Response surface methodology (RSM),Cross training


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