期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 40, 期 4, 页码 484-496出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2010.546540
关键词
Best subset method; Information criteria; Supersaturated designs; Variable selection
Supersaturated designs are a large class of factorial designs which can be used for screening out the important factors from a large set of potentially active variables. The huge advantage of these designs is that they reduce the experimental cost drastically, but their critical disadvantage is the confounding involved in the statistical analysis. In this article, we propose a method for analyzing data using several types of supersaturated designs. Modifications of widely used information criteria are given and applied to the variable selection procedure for the identification of the active factors. The effectiveness of the proposed method is depicted via simulated experiments and comparisons.
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