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 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 ca...

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Bibliographic Details
Published in:Computational statistics 2013-08, Vol.28 (4), p.1835-1852
Main Authors: Yan, Changrong, Zhang, Dixin
Format: Article
Language:English
Subjects:
Accuracy
Computer simulation
Data processing
Economic Theory/Quantitative Economics/Mathematical Methods
Estimates
Estimating techniques
Mathematics and Statistics
Maximum likelihood method
Original Paper
Principal components analysis
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Reduction
Regression analysis
Roles
Statistics
Studies
Subspaces
Survival
Survival analysis
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-012-0383-4