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- Publisher Website: 10.1198/jasa.2009.tm09372
- Scopus: eid_2-s2.0-77952562608
- WOS: WOS:000276786500026
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Article: Dimension Reduction and Semiparametric Estimation of Survival Models
Title | Dimension Reduction and Semiparametric Estimation of Survival Models |
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Authors | |
Keywords | Censored Data Hazard Function Linear Transformation Model Nonparametric Regression |
Issue Date | 2010 |
Publisher | American 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/221686 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 3.922 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xia, Y | - |
dc.contributor.author | Zhang, D | - |
dc.contributor.author | Xu, J | - |
dc.date.accessioned | 2015-12-04T15:29:07Z | - |
dc.date.available | 2015-12-04T15:29:07Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Journal of the American Statistical Association, 2010, v. 105, p. 278-290 | - |
dc.identifier.issn | 0162-1459 | - |
dc.identifier.uri | http://hdl.handle.net/10722/221686 | - |
dc.description.abstract | In 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.language | eng | - |
dc.publisher | American Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main | - |
dc.relation.ispartof | Journal of the American Statistical Association | - |
dc.subject | Censored Data | - |
dc.subject | Hazard Function | - |
dc.subject | Linear Transformation Model | - |
dc.subject | Nonparametric Regression | - |
dc.title | Dimension Reduction and Semiparametric Estimation of Survival Models | - |
dc.type | Article | - |
dc.identifier.email | Xu, J: xujf@hku.hk | - |
dc.identifier.authority | Xu, J=rp02086 | - |
dc.identifier.doi | 10.1198/jasa.2009.tm09372 | - |
dc.identifier.scopus | eid_2-s2.0-77952562608 | - |
dc.identifier.volume | 105 | - |
dc.identifier.spage | 278 | - |
dc.identifier.epage | 290 | - |
dc.identifier.isi | WOS:000276786500026 | - |
dc.identifier.issnl | 0162-1459 | - |