Dimension reduction and semiparametric estimation of survival models

Yingcun Xia; Dixin Zhang; Jinfeng Xu

doi:10.1198/jasa.2009.tm09372

Dimension reduction and semiparametric estimation of survival models

Yingcun Xia, Dixin Zhang, Jinfeng Xu

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

37 Citations (Scopus)

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. © 2010 American Statistical Association.

Original language	English
Pages (from-to)	278-290
Journal	Journal of the American Statistical Association
Volume	105
Issue number	489
DOIs	https://doi.org/10.1198/jasa.2009.tm09372
Publication status	Published - Mar 2010
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Censored data
Hazard function
Linear transformation model
Nonparametric regression

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10.1198/jasa.2009.tm09372

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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. {\textcopyright} 2010 American Statistical Association.",

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AU - Xia, Yingcun

AU - Zhang, Dixin

AU - Xu, Jinfeng

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - 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. © 2010 American Statistical Association.

AB - 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. © 2010 American Statistical Association.

KW - Censored data

KW - Hazard function

KW - Linear transformation model

KW - Nonparametric regression

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DO - 10.1198/jasa.2009.tm09372

M3 - RGC 21 - Publication in refereed journal

SN - 0162-1459

VL - 105

SP - 278

EP - 290

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 489

ER -

Dimension reduction and semiparametric estimation of survival models

Abstract

Bibliographical note

Research Keywords

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