Rank test statistics for unbalanced nested designs

Stavropoulos, A; Caroni, C

dc.contributor.author	Stavropoulos, A	en
dc.contributor.author	Caroni, C	en
dc.date.accessioned	2014-03-01T01:29:04Z
dc.date.available	2014-03-01T01:29:04Z
dc.date.issued	2008	en
dc.identifier.issn	15723127	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19115
dc.subject	ANOVA	en
dc.subject	Nested designs	en
dc.subject	Nonparametric hypotheses	en
dc.subject	Rank test statistics	en
dc.title	Rank test statistics for unbalanced nested designs	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.stamet.2007.06.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.stamet.2007.06.001	en
heal.publicationDate	2008	en
heal.abstract	We formulate rank statistics for testing hypotheses in unbalanced, and possibly heteroscedastic, two-factor nested designs with independent observations. These include Wald-type statistics based on the theory introduced by Akritas, Arnold and Brunner, as well as a Box-type approximation which is intended to improve the accuracy of approximation to asymptotic distributions. We also present statistics based on a recent theory of weighted F-statistics for ranks. The actual sizes of the statistics at various nominal levels are compared in a simulation study. Our main conclusion is that the Box-adjusted Wald-type statistic is the only statistic that is accurate across all the situations considered and therefore we recommend it for general use. © 2007 Elsevier B.V. All rights reserved.	en
heal.journalName	Statistical Methodology	en
dc.identifier.doi	10.1016/j.stamet.2007.06.001	en
dc.identifier.volume	5	en
dc.identifier.issue	2	en
dc.identifier.spage	93	en
dc.identifier.epage	105	en