On testing independence with right truncated data

dc.citation.epage3206en_US
dc.citation.issueNumber5en_US
dc.citation.spage3201en_US
dc.citation.volumeNumber30en_US
dc.contributor.authorGurler, U.en_US
dc.date.accessioned2015-07-28T11:56:28Z
dc.date.available2015-07-28T11:56:28Z
dc.date.issued1997-12en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractInference with bivariate data gained considerable interest recently, See eg.[1],[10],[12]. All of these studies howrver consider estimation of the bivariate distribution function under various bivariate censoring models. Recently (:iirler[7,8] considered estimation of the bivariate distribution and the hazard functions under trunc.atlon/censoring models. The purpose of this study is to investigate procedures for testing the independence of I hc components of the bivariate vector for truncated data. To this end, further properties of the bivariate functrouals introduced in GiirleQ] are elaborated. Two alternative methods for hypothesis testing are suggested aud some large sample properties are derived. The procedures suggested in this paper are applicable to left/right. truncated and left truncated right censored data. However to keep the presentation simple we ~~oufinr t.hr discussion to the right truncated case. Also, to avoid technicalities, it is assumed that all the univariat.e and the bivariate distribution functions are absolutely continuous admitting densities.en_US
dc.description.provenanceMade available in DSpace on 2015-07-28T11:56:28Z (GMT). No. of bitstreams: 1 10.1016-S0362-546X(97)00414-8.pdf: 293380 bytes, checksum: 1afaee84255e842e7cd869d0929ef3cb (MD5)en
dc.identifier.doi10.1016/S0362-546X(97)00414-8en_US
dc.identifier.issn0362-546X
dc.identifier.urihttp://hdl.handle.net/11693/10990
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/S0362-546X(97)00414-8en_US
dc.source.titleNonlinear Analysis: Theory, Methods and Applicationsen_US
dc.subjectRight Truncationen_US
dc.subjectTest Of Independenceen_US
dc.subjectReverse Hazarden_US
dc.subjectNonparametric Estimationen_US
dc.titleOn testing independence with right truncated dataen_US
dc.typeArticleen_US

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