Sufficient dimension reduction for censored regressions |
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Authors: | Lu Wenbin Li Lexin |
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Institution: | Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA. lu@stat.ncsu.edu |
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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. |
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Keywords: | Censored data Central subspace Inverse censoring probability weighted estimation Sliced inverse regression Sufficient dimension reduction |
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