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Article: Goodness-of-fit tests for the Cox model via bootstrap method

TitleGoodness-of-fit tests for the Cox model via bootstrap method
Authors
KeywordsBootstrap
Cumulative Hazard Process
Empirical Process
Goodness-Of-Fit
Partial Likelihood
Issue Date1995
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspi
Citation
Journal Of Statistical Planning And Inference, 1995, v. 47 n. 3, p. 237-256 How to Cite?
AbstractGlobal goodness-of-fit tests for the Cox model, in which no partitions of the time and covariate spaces into cells are needed, are established. Test statistics are based upon the cumulative hazard process under the censored survival data. Although this process converges to a Gaussian process almost surely, a problem arises from the fact that the asymptotic covariance structure depends upon the underlying distribution which is unspecified. Therefore, a bootstrap method suggested by Efron (1981) is employed. It can be shown that the bootstrapped cumulative hazard process converges weakly to the same Gaussian process. As an illustration, the proposed tests are applied to analyze the Stanford heart transplant data. Finally, a discussion for dealing with discrete covariate is given. © 1995.
Persistent Identifierhttp://hdl.handle.net/10722/172366
ISSN
2015 Impact Factor: 0.727
2015 SCImago Journal Rankings: 1.090

 

DC FieldValueLanguage
dc.contributor.authorBurke, MDen_US
dc.contributor.authorYuen, KCen_US
dc.date.accessioned2012-10-30T06:22:10Z-
dc.date.available2012-10-30T06:22:10Z-
dc.date.issued1995en_US
dc.identifier.citationJournal Of Statistical Planning And Inference, 1995, v. 47 n. 3, p. 237-256en_US
dc.identifier.issn0378-3758en_US
dc.identifier.urihttp://hdl.handle.net/10722/172366-
dc.description.abstractGlobal goodness-of-fit tests for the Cox model, in which no partitions of the time and covariate spaces into cells are needed, are established. Test statistics are based upon the cumulative hazard process under the censored survival data. Although this process converges to a Gaussian process almost surely, a problem arises from the fact that the asymptotic covariance structure depends upon the underlying distribution which is unspecified. Therefore, a bootstrap method suggested by Efron (1981) is employed. It can be shown that the bootstrapped cumulative hazard process converges weakly to the same Gaussian process. As an illustration, the proposed tests are applied to analyze the Stanford heart transplant data. Finally, a discussion for dealing with discrete covariate is given. © 1995.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspien_US
dc.relation.ispartofJournal of Statistical Planning and Inferenceen_US
dc.subjectBootstrapen_US
dc.subjectCumulative Hazard Processen_US
dc.subjectEmpirical Processen_US
dc.subjectGoodness-Of-Fiten_US
dc.subjectPartial Likelihooden_US
dc.titleGoodness-of-fit tests for the Cox model via bootstrap methoden_US
dc.typeArticleen_US
dc.identifier.emailYuen, KC: kcyuen@hku.hken_US
dc.identifier.authorityYuen, KC=rp00836en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0001581912en_US
dc.identifier.volume47en_US
dc.identifier.issue3en_US
dc.identifier.spage237en_US
dc.identifier.epage256en_US
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridBurke, MD=7402866439en_US
dc.identifier.scopusauthoridYuen, KC=7202333703en_US

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