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EXPLOITING THE INHERENT STRUCTURE IN ROBUST PARAMETER DESIGN EXPERIMENTS

J. Bérubé and V. N. Nair
Statistica Sinica
Vol. 8, No. 1 (January 1998), pp. 43-66
Stable URL: http://www.jstor.org/stable/24306341
Page Count: 24
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
EXPLOITING THE INHERENT STRUCTURE IN ROBUST PARAMETER DESIGN EXPERIMENTS
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Abstract

Robust parameter design methods have been used successfully in industry for some time. Despite this, there has been some skepticism in the statistical literature about the feasibility of conducting industrial experiments to estimate both location and dispersion effects. It has been claimed that a large experimental run size is needed to estimate dispersion effects and that such experiments are not pratical in industry where the emphasis is on studying many factors simultaneously using highly fractionated designs. We show in this paper that this misconception arises from the fact that the commonly used methods of analysis ignore the basic structure in parameter design studies and hence are unnecessarily inefficient. We consider different models and methods of analysis and quantify the gains to be made from exploiting the inherent structure in parameter design studies. The consequences of these conclusions for the planning of such studies are also discussed.

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