期刊
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
卷 139, 期 7, 页码 2362-2372出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jspi.2008.10.023
关键词
Akaike information criterion; Dantzig selector; Factor sparsity; Linear programming; Profile plot; Screening experiment; Supersaturated design
资金
- National Science Foundation [DMS-0505728, DMS-0806137]
- Direct For Mathematical & Physical Scien [0806137] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [0806137] Funding Source: National Science Foundation
A supersaturated design is a design whose run size is not enough for estimating all the main effects. It is commonly used in screening experiments. where the goals are to identify sparse and dominant active factors with low cost. In this paper, we study a variable selection method via the Dantzig selector, proposed by Candes and Tao [2007. The Dantzig selector: statistical estimation when p is much larger than n. Annals of Statistics 35. 2313-2351], to screen important effects. A graphical procedure and an automated procedure are suggested to accompany with the method. Simulation shows that this method performs well compared to existing methods in the literature and is more efficient at estimating the model size. (C) 2008 Elsevier B.V. All rights reserved.
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