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dc.contributor.CRUESPUniversidade Estadual de Campinaspt_BR
dc.typeArtigo de periódicopt_BR
dc.titleDecomposability of high-dimensional diversity measures: Quasi-U-statistics, martingales and nonstandard asymptoticspt_BR
dc.contributor.authorPinheiro, Apt_BR
dc.contributor.authorSen, PKpt_BR
dc.contributor.authorPinheiro, HPpt_BR
unicamp.author.emailpinheiro@ime.unicamp.brpt_BR
unicamp.authorPinheiro, Aluisio Pinheiro, Hildete Prisco Univ Estadual Campinas, Dept Stat, IMECC, Campinas, SP, Brazilpt_BR
unicamp.authorSen, Pranab Kumar Univ N Carolina, Dept Biostat, Chapel Hill, NC USApt_BR
unicamp.authorSen, Pranab Kumar Univ N Carolina, Dept Stat & Operat Res, Chapel Hill, NC USApt_BR
dc.subjectCategorical Datapt_BR
dc.subjectDependencept_BR
dc.subjectDNApt_BR
dc.subjectGenomicspt_BR
dc.subjectHamming distancept_BR
dc.subjectOrthogonal systempt_BR
dc.subjectPermutation measurept_BR
dc.subjectSecond-order asymptoticspt_BR
dc.subjectSecond-order decomposabilitypt_BR
dc.subject.wosGenomept_BR
dc.description.abstractIn analyses of complex diversity, especially that arising in genetics, genomics, ecology and other high-dimensional (and sometimes low-sample-size) data models, typically subgroup decomposability (analogous to ANOVA decomposability) arises. For group divergence of diversity measures in a high-dimension low-sample-size scenario, it is shown that Hamming distance type statistics lead to a general class of quasi-U-statistics having, under the hypothesis of homogeneity, a martingale (array) property, providing a key to the study of general (nonstandard) asymptotics. Neither the stochastic independence nor homogeneity of the marginal probability laws plays a basic role. A genomic MANOVA model is presented as an illustration. (C) 2009 Elsevier Inc. All rights reserved.pt
dc.relation.ispartofJournal Of Multivariate Analysispt_BR
dc.relation.ispartofabbreviationJ. Multivar. Anal.pt_BR
dc.publisher.citySan Diegopt_BR
dc.publisher.countryEUApt_BR
dc.publisherElsevier Incpt_BR
dc.date.issued2009pt_BR
dc.date.monthofcirculationSEPpt_BR
dc.identifier.citationJournal Of Multivariate Analysis. Elsevier Inc, v. 100, n. 8, n. 1645, n. 1656, 2009.pt_BR
dc.language.isoenpt_BR
dc.description.volume100pt_BR
dc.description.issuenumber8pt_BR
dc.description.firstpage1645pt_BR
dc.description.lastpage1656pt_BR
dc.rightsfechadopt_BR
dc.rights.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policypt_BR
dc.sourceWeb of Sciencept_BR
dc.identifier.issn0047-259Xpt_BR
dc.identifier.wosidWOS:000266565200006pt_BR
dc.identifier.doi10.1016/j.jmva.2009.01.007pt_BR
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorshipFAEP-UNICAMP [412/04]pt_BR
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsordocumentnumberFAPESP [03/10105_2, 06/04897_1]pt
dc.description.sponsordocumentnumberFAEP-UNICAMP [412/04]pt
dc.description.sponsordocumentnumberCNPq [474329/2004_6]pt
dc.date.available2014-11-15T23:03:12Z
dc.date.available2015-11-26T16:16:50Z-
dc.date.accessioned2014-11-15T23:03:12Z
dc.date.accessioned2015-11-26T16:16:50Z-
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dc.description.provenanceMade available in DSpace on 2015-11-26T16:16:50Z (GMT). No. of bitstreams: 2 WOS000266565200006.pdf: 711630 bytes, checksum: 844433304a1210a2305088f94a5e8c8f (MD5) WOS000266565200006.pdf.txt: 38213 bytes, checksum: 7b4c049607aa96aaed4f7fbfc87da110 (MD5) Previous issue date: 2009en
dc.identifier.urihttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/79332pt_BR
dc.identifier.urihttp://www.repositorio.unicamp.br/handle/REPOSIP/79332
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/79332-
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