Sparse dimension reduction for survival data

作者：Yan Changrong; Zhang Dixin^*

来源：Computational Statistics, 2013, 28(4): 1835-1852.

DOI：10.1007/s00180-012-0383-4

• Censored data
• Hazard function
• Variable selection
• Dimension reduction

In this paper, we study the estimation and variable selection of the sufficient dimension reduction space for survival data via a new combination of 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.

• 出版日期2013-8
• 单位南京大学