A variable selection method for analyzing supersaturated designs

Koukouvinos, C; Mylona, K; Skountzou, A

dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Mylona, K	en
dc.contributor.author	Skountzou, A	en
dc.date.accessioned	2014-03-01T01:35:03Z
dc.date.available	2014-03-01T01:35:03Z
dc.date.issued	2011	en
dc.identifier.issn	0361-0918	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20957
dc.subject	Best subset method	en
dc.subject	Information criteria	en
dc.subject	Supersaturated designs	en
dc.subject	Variable selection	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	Active variables	en
dc.subject.other	Best subset method	en
dc.subject.other	Factorial design	en
dc.subject.other	Information criterion	en
dc.subject.other	Large class	en
dc.subject.other	Simulated experiments	en
dc.subject.other	Statistical analysis	en
dc.subject.other	Supersaturated designs	en
dc.subject.other	Variable selection	en
dc.subject.other	Variable selection methods	en
dc.subject.other	Design	en
dc.title	A variable selection method for analyzing supersaturated designs	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/03610918.2010.546540	en
heal.identifier.secondary	http://dx.doi.org/10.1080/03610918.2010.546540	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	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. Copyright © 2011 Taylor & Francis Group, LLC.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	Communications in Statistics: Simulation and Computation	en
dc.identifier.doi	10.1080/03610918.2010.546540	en
dc.identifier.isi	ISI:000288264100002	en
dc.identifier.volume	40	en
dc.identifier.issue	4	en
dc.identifier.spage	484	en
dc.identifier.epage	496	en