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Article: Dimension Reduction and Semiparametric Estimation of Survival Models

TitleDimension Reduction and Semiparametric Estimation of Survival Models
Authors
KeywordsCensored Data
Hazard Function
Linear Transformation Model
Nonparametric Regression
Issue Date2010
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal of the American Statistical Association, 2010, v. 105, p. 278-290 How to Cite?
AbstractIn this paper, we propose a new dimension reduction method by introducing a nominal regression model with the hazard function as the conditional mean, which naturally retrieves information from complete data and censored data as well. Moreover, without requiring the linearity condition, the new method can estimate the entire central subspace consistently and exhaustively. The method also provides an alternative approach for the analysis of censored data assuming neither the link function nor the distribution. Hence, it exhibits superior robustness properties. Numerical studies show that the method can indeed be readily used to efficiently estimate survival models, explore the data structures and identify important variables.
Persistent Identifierhttp://hdl.handle.net/10722/221686
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXia, Y-
dc.contributor.authorZhang, D-
dc.contributor.authorXu, J-
dc.date.accessioned2015-12-04T15:29:07Z-
dc.date.available2015-12-04T15:29:07Z-
dc.date.issued2010-
dc.identifier.citationJournal of the American Statistical Association, 2010, v. 105, p. 278-290-
dc.identifier.issn0162-1459-
dc.identifier.urihttp://hdl.handle.net/10722/221686-
dc.description.abstractIn this paper, we propose a new dimension reduction method by introducing a nominal regression model with the hazard function as the conditional mean, which naturally retrieves information from complete data and censored data as well. Moreover, without requiring the linearity condition, the new method can estimate the entire central subspace consistently and exhaustively. The method also provides an alternative approach for the analysis of censored data assuming neither the link function nor the distribution. Hence, it exhibits superior robustness properties. Numerical studies show that the method can indeed be readily used to efficiently estimate survival models, explore the data structures and identify important variables.-
dc.languageeng-
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main-
dc.relation.ispartofJournal of the American Statistical Association-
dc.subjectCensored Data-
dc.subjectHazard Function-
dc.subjectLinear Transformation Model-
dc.subjectNonparametric Regression-
dc.titleDimension Reduction and Semiparametric Estimation of Survival Models-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1198/jasa.2009.tm09372-
dc.identifier.scopuseid_2-s2.0-77952562608-
dc.identifier.volume105-
dc.identifier.spage278-
dc.identifier.epage290-
dc.identifier.isiWOS:000276786500026-

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