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Sufficient dimension reduction for censored regressions.

ABSTRACT: Methodology of sufficient dimension reduction (SDR) has offered an effective means to facilitate regression analysis of high-dimensional data. When the response is censored, however, most existing SDR estimators cannot be applied, or require some restrictive conditions. In this article, we propose a new class of inverse censoring probability weighted SDR estimators for censored regressions. Moreover, regularization is introduced to achieve simultaneous variable selection and dimension reduction. Asymptotic properties and empirical performance of the proposed methods are examined.

SUBMITTER: Lu W

PROVIDER: S-EPMC3018713 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Sufficient dimension reduction for censored regressions.

Lu Wenbin W Li Lexin L

Biometrics 20100928 2

Methodology of sufficient dimension reduction (SDR) has offered an effective means to facilitate regression analysis of high-dimensional data. When the response is censored, however, most existing SDR estimators cannot be applied, or require some restrictive conditions. In this article, we propose a new class of inverse censoring probability weighted SDR estimators for censored regressions. Moreover, regularization is introduced to achieve simultaneous variable selection and dimension reduction. ...[more]

PMID: 20880013

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