Experimental Designs for Estimating Both Mean and Variance Functions
作者:
ViningG. Geoffrey,
SchaubDiane,
期刊:
Journal of Quality Technology
(Taylor Available online 1996)
卷期:
Volume 28,
issue 2
页码: 135-147
ISSN:0022-4065
年代: 1996
DOI:10.1080/00224065.1996.11979654
出版商: Taylor&Francis
关键词: D-Optimality;Multiple Responses;Response Surface Methodology;Simultaneous Optimization;Taguchi Methods
数据来源: Taylor
摘要:
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.
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