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dc.contributor.authorEmura, Takeshien_US
dc.contributor.authorWang, Weijingen_US
dc.contributor.authorHung, Hui-Nienen_US
dc.date.accessioned2014-12-08T15:38:01Z-
dc.date.available2014-12-08T15:38:01Z-
dc.date.issued2011-01-01en_US
dc.identifier.issn1017-0405en_US
dc.identifier.urihttp://hdl.handle.net/11536/26101-
dc.description.abstractWe investigate the dependent relationship between two failure time variables that truncate each other. Chaieb, Rivest, and Abdous (2006) proposed a semi-parametric model under the so-called "semi-survival" Archimedean-copula assumption and discussed estimation of the association parameter, the truncation probability, and the marginal functions. Here the same model assumption is adopted but different inference approaches are proposed. For estimating the association parameter, we extend the conditional likelihood approach (Clayton (1978)) and the two-by-two table approach (Wang (2003)) to dependent truncation data. We further show that the three estimators, including that proposed by Chaieb, Rivest, and Abdous (2006), differ in weights. The likelihood approach provides the formula for a good weight. Large sample properties of the proposed methods are established by applying the functional delta method, which can handle estimating functions that are not in the form of U-statistics. Analytic formulae for the asymptotic variance estimators are provided. Two competing methods are compared via simulations, and applied to the transfusion-related AIDS data.en_US
dc.language.isoen_USen_US
dc.subjectArchimedean copula modelen_US
dc.subjectconditional likelihooden_US
dc.subjectfunctional delta methoden_US
dc.subjectKendall's tauen_US
dc.subjecttruncation dataen_US
dc.subjecttwo-by-two tableen_US
dc.titleSEMI-PARAMETRIC INFERENCE FOR COPULA MODELS FOR TRUNCATED DATAen_US
dc.typeArticleen_US
dc.identifier.journalSTATISTICA SINICAen_US
dc.citation.volume21en_US
dc.citation.issue1en_US
dc.citation.spage349en_US
dc.citation.epage367en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000287434900016-
dc.citation.woscount3-
Appears in Collections:Articles