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Title: Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates
Authors: Wang, Shanshan
Xiang, Liming
Keywords: Penalized empirical likelihood
Empirical likelihood ratio
Issue Date: 2016
Source: Wang, S., & Xiang, L. Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates. Statistics and Computing, in press.
Series/Report no.: Statistics and Computing
Abstract: High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by applications in high-throughput genomic data analysis. In this paper, we propose a class of regularization methods, integrating both the penalized empirical likelihood and pseudoscore approaches, for variable selection and estimation in sparse and high-dimensional additive hazards regression models. When the number of covariates grows with the sample size, we establish asymptotic properties of the resulting estimator and the oracle property of the proposed method. It is shown that the proposed estimator is more efficient than that obtained from the non-concave penalized likelihood approach in the literature. Based on a penalized empirical likelihood ratio statistic, we further develop a nonparametric likelihood approach for testing the linear hypothesis of regression coefficients and constructing confidence regions consequently. Simulation studies are carried out to evaluate the performance of the proposed methodology and also two real data sets are analyzed.
ISSN: 0960-3174
DOI: 10.1007/s11222-016-9690-x
Schools: School of Physical and Mathematical Sciences 
Rights: © 2016 Springer Science+Business Media New York. This is the author created version of a work that has been peer reviewed and accepted for publication by Statistics and Computing, Springer Science+Business Media New York. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
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