Sparse dimension reduction for survival data

Changrong Yan () and Dixin Zhang ()

Computational Statistics, 2013, vol. 28, issue 4, 1835-1852

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

Keywords: Censored data; Hazard function; Variable selection; Dimension reduction (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:4:p:1835-1852

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DOI: 10.1007/s00180-012-0383-4

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