Premium
Sufficient Dimension Reduction for Censored Regressions
Author(s) -
Lu Wenbin,
Li Lexin
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01490.x
Subject(s) - sliced inverse regression , estimator , censoring (clinical trials) , dimensionality reduction , sufficient dimension reduction , mathematics , dimension (graph theory) , reduction (mathematics) , feature selection , regularization (linguistics) , statistics , regression , computer science , econometrics , mathematical optimization , artificial intelligence , geometry , pure mathematics
Summary 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.