Sparse dimension reduction for survival data

Authors:
Changrong Yan;Dixin Zhang
Affiliations:
Department of Finance and Insurance, School of Economics, Center of Research of Finance Econometrics and Risk Management, Nanjing University, Nanjing, China;Department of Finance and Insurance, School of Economics, Center of Research of Finance Econometrics and Risk Management, Nanjing University, Nanjing, China
Venue:
Computational Statistics
Year:
2013

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Abstract

In this paper, we study the estimation and variable selection of the sufficient dimension reduction space for survival data via a new combination of $$L_1$$ penalty and the refined outer product of gradient method (rOPG; Xia et al. in J R Stat Soc Ser B 64:363---410, 2002), called SH-OPG hereafter. SH-OPG can exhaustively estimate the central subspace and select the informative covariates simultaneously; Meanwhile, the estimated directions remain orthogonal automatically after dropping noninformative regressors. The efficiency of SH-OPG is verified through extensive simulation studies and real data analysis.