Sparse dimension reduction for survival data
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 64:363410, 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.
dimension reduction variable selection censored data hazard function