Nonparametric models and methods for ancova with dependent data

Tsangari, H; Akritas, MG

dc.contributor.author	Tsangari, H	en
dc.contributor.author	Akritas, MG	en
dc.date.accessioned	2014-03-01T02:49:50Z
dc.date.available	2014-03-01T02:49:50Z
dc.date.issued	2004	en
dc.identifier.issn	10485252	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34755
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-2542559652&partnerID=40&md5=069d2e301bbc0c18cbd8eb57d9a15a34	en
dc.subject	Kernel smoothing	en
dc.subject	Longitudinal data	en
dc.subject	Mixed model	en
dc.subject	Nonparametric hypotheses	en
dc.subject	Nonparametric regression	en
dc.subject	Repeated measures designs	en
dc.title	Nonparametric models and methods for ancova with dependent data	en
heal.type	conferenceItem	en
heal.publicationDate	2004	en
heal.abstract	The nonparametric ANCOVA model of Akritas et al. [Akritas, M. G., Arnold, S. F. and Du, Y. (2000). Nonparametric models and methods for nonlinear analysis of covariance. Biometrika, 87(3), 507-526.] is extended to longitudinal data and for up to three covariates. In this model the response distributions need not be continuous or to comply to any parametric or semiparainetric model. The nonparametric covariate effect can be different in different factor level combinations. Nonparametric hypotheses of no main factor effects, no interaction and no simple effect, which adjust for the covariate values, are considered. The test statistics, which are based on averages over the covariate values of certain Nadaraya-Watson regression quantities, have asymptotically a central chi-squared distribution under their respective null hypotheses. Small sample corrections to the asymptotic distribution are provided. Simulation results and data analysis for a real dataset are presented.	en
heal.journalName	Journal of Nonparametric Statistics	en
dc.identifier.volume	16	en
dc.identifier.issue	3-4	en
dc.identifier.spage	403	en
dc.identifier.epage	420	en