期刊
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
卷 137, 期 6, 页码 2068-2079出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jspi.2006.06.038
关键词
mixed-level; partial least-squares; supersaturated design; variable selection; variable importance in projection
A supersaturated design (SSD) is a design whose run size is not enough for estimating all the main effects. The goal in conducting such a design is to identify, presumably only a few, relatively dominant active effects with a cost as low as possible. However, data analysis of such designs remains primitive: traditional approaches are not appropriate in Such a situation and several methods which were proposed in the literature in recent years are effective when used to analyze two-level SSDs. In this paper, we introduce a variable selection procedure, called the PLSVS method, to screen active effects in mixed-level SSDs based on the variable importance in projection which is an important concept in the partial least-squares regression. Simulation studies show that this procedure is effective. (c) 2006 Elsevier B.V. All rights reserved.
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