Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates
作者:Wang, SS(Wang, Shanshan);Xiang, LM(Xiang, Liming)
STATISTICS AND COMPUTING
DOI:10.1007/s11222-016-9690-x
出版年:SEP 2017
摘要
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.
SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
研究方向:Computer Science; Mathematics
Web of Science 类别:Computer Science, Theory & Methods; Statistics & Probability
