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Sparse dimension reduction for survival data

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  • Changrong Yan

  • Dixin Zhang

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. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Changrong Yan & Dixin Zhang, 2013. "Sparse dimension reduction for survival data," Computational Statistics, Springer, vol. 28(4), pages 1835-1852, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1835-1852
    DOI: 10.1007/s00180-012-0383-4
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    References listed on IDEAS

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