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Sparse dimension reduction for survival data

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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.

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Acknowledgments

The authors are very grateful to the Editor, the AE, two anonymous referees and professor Y. Xia for helpful comments and constructive advices.

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Authors and Affiliations

  1. Department of Finance and Insurance, School of Economics, Center of Research of Finance Econometrics and Risk Management, Nanjing University, Nanjing, China

    Changrong Yan & Dixin Zhang

Authors
  1. Changrong Yan
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  2. Dixin Zhang
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Corresponding author

Correspondence to Dixin Zhang.

Additional information

This work is supported by the National Natural Science Foundation of China, grant number 11071113.

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Yan, C., Zhang, D. Sparse dimension reduction for survival data. Comput Stat 28, 1835–1852 (2013). https://doi.org/10.1007/s00180-012-0383-4

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