Group screening method for the statistical analysis of E (fN O D)-optimal mixed-level supersaturated designs

Koukouvinos, C; Mylona, K

dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Mylona, K	en
dc.date.accessioned	2014-03-01T01:30:50Z
dc.date.available	2014-03-01T01:30:50Z
dc.date.issued	2009	en
dc.identifier.issn	15723127	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19638
dc.subject	Data analysis	en
dc.subject	Group screening method	en
dc.subject	Penalized least squares	en
dc.subject	Supersaturated design	en
dc.title	Group screening method for the statistical analysis of E (fN O D)-optimal mixed-level supersaturated designs	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.stamet.2008.12.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.stamet.2008.12.002	en
heal.publicationDate	2009	en
heal.abstract	In this paper, we propose the application of group screening methods for analyzing data using E (fN O D)-optimal mixed-level supersaturated designs possessing the equal occurrence property. 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 high degree of confounding among factorial effects. Based on the idea of the group screening methods, the f factors are sub-divided into g ""group-factors"". The ""group-factors"" are then studied using the penalized likelihood statistical analysis methods at a factorial design with orthogonal or near-orthogonal columns. All factors in groups found to have a large effect are then studied in a second stage of experiments. A comparison of the Type I and Type II error rates of various estimation methods via simulation experiments is performed. The results are presented in tables and discussion follows. © 2008 Elsevier B.V. All rights reserved.	en
heal.journalName	Statistical Methodology	en
dc.identifier.doi	10.1016/j.stamet.2008.12.002	en
dc.identifier.volume	6	en
dc.identifier.issue	4	en
dc.identifier.spage	380	en
dc.identifier.epage	388	en