Analyzing supersaturated designs with entropic measures

Koukouvinos, C; Massou, E; Mylona, K; Parpoula, C

dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Massou, E	en
dc.contributor.author	Mylona, K	en
dc.contributor.author	Parpoula, C	en
dc.date.accessioned	2014-03-01T01:35:18Z
dc.date.available	2014-03-01T01:35:18Z
dc.date.issued	2011	en
dc.identifier.issn	0378-3758	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20990
dc.subject	Error rates	en
dc.subject	Factor screening	en
dc.subject	Generalized linear models	en
dc.subject	Havrda-Charvát entropy	en
dc.subject	Information gain	en
dc.subject	Rényi entropy	en
dc.subject	Supersaturated design	en
dc.subject	Tsallis entropy	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	CONSTRUCTION	en
dc.subject.other	DIVERGENCE	en
dc.subject.other	STRATEGY	en
dc.title	Analyzing supersaturated designs with entropic measures	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jspi.2010.10.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jspi.2010.10.001	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	A supersaturated design is a design for which there are fewer runs than effects to be estimated. In this paper, we propose a method for screening out the important factors from a large set of potentially active variables, based on an information theoretical approach. Three entropy measures: Renyi entropy, Tsallis entropy and Havrda-Charvat entropy, have been associated with the measure of information gain, in order to identify the significant factors using data and assuming generalized linear models. The investigation of the proposed method performance and the comparison of each entropic measure application have been accomplished through simulation experiments. A noteworthy advantage of this paper is the use of generalized linear models for analyzing data from supersaturated designs, a fact that, to the best of our knowledge, has not yet been studied. (C) 2010 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Journal of Statistical Planning and Inference	en
dc.identifier.doi	10.1016/j.jspi.2010.10.001	en
dc.identifier.isi	ISI:000285227100021	en
dc.identifier.volume	141	en
dc.identifier.issue	3	en
dc.identifier.spage	1307	en
dc.identifier.epage	1312	en