Mean squared error of prediction approach to the analysis of a combined array
作者:
EileenM. O'Donnell,
G. Geoffrey Vining,
期刊:
Journal of Applied Statistics
(Taylor Available online 1997)
卷期:
Volume 24,
issue 6
页码: 733-746
ISSN:0266-4763
年代: 1997
DOI:10.1080/02664769723468
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The combined array provides a powerful, more statistically rigorous alternative to Taguchi's crossed-array approach to robust parameter design. The combined array assumes a single linear model in the control and the noise factors. One may then find conditions for the control factors which will minimize an appropriate loss function that involves the noise factors. The most appropriate loss function is often simply the resulting process variance, recognizing that the noise factors are actually random effects in the process. Because the major focus of such an experiment is to optimize the estimated process variance, it is vital to understand the resulting prediction properties. This paper develops the mean squared error for the estimated process variance for the combined array approach, under the assumption that the model is correctly specified. Specific combined arrays are compared for robustness. A practical example outlines how this approach may be used to select appropriate combined arrays within a particular experimental situation.
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