A lower bound to the measure of optimality for main effect plans in the symmetric factorial experiments

Chatterjee, K; Evangelaras, H; Koukouvinos, C

dc.contributor.author	Chatterjee, K	en
dc.contributor.author	Evangelaras, H	en
dc.contributor.author	Koukouvinos, C	en
dc.date.accessioned	2014-03-01T01:34:54Z
dc.date.available	2014-03-01T01:34:54Z
dc.date.issued	2011	en
dc.identifier.issn	0361-0926	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20926
dc.subject	MS-optimality	en
dc.subject	Orthogonal arrays	en
dc.subject	Trace	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	D-optimal	en
dc.subject.other	Factorial experiment	en
dc.subject.other	Information matrix	en
dc.subject.other	Lower bounds	en
dc.subject.other	Main effect	en
dc.subject.other	MS-optimality	en
dc.subject.other	Optimality	en
dc.subject.other	Orthogonal array	en
dc.subject.other	Orthogonal arrays	en
dc.subject.other	Trace	en
dc.subject.other	Optimization	en
dc.title	A lower bound to the measure of optimality for main effect plans in the symmetric factorial experiments	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/03610921003778233	en
heal.identifier.secondary	http://dx.doi.org/10.1080/03610921003778233	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	A design d is called D-optimal if it maximizes det(Md), and is called MS-optimal if it maximizes tr(Md) and minimizes tr[(Md)2] among those which maximize tr(Md), where Md stands for the information matrix produced from d under a given model. In this article, we establish a lower bound for tr[(Md)2] with respect to a main effects model, where d is an s-level symmetric orthogonal array of strength at least one. Non isomorphic two level MS-optimal orthogonal arrays of strength one with N=10, 14, and 18 runs, non isomorphic three level MS-optimal orthogonal arrays of strength one with N=6, 12, and 15 runs and non isomorphic four level MS-optimal orthogonal arrays of strength one with N=12 runs are also presented. Copyright © Taylor & Francis Group, LLC.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	Communications in Statistics - Theory and Methods	en
dc.identifier.doi	10.1080/03610921003778233	en
dc.identifier.isi	ISI:000289834100008	en
dc.identifier.volume	40	en
dc.identifier.issue	13	en
dc.identifier.spage	2358	en
dc.identifier.epage	2365	en