Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Shanshanen
dc.contributor.authorXiang, Limingen
dc.identifier.citationWang, S., & Xiang, L. Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates. Statistics and Computing, in press.en
dc.description.abstractHigh-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.en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.format.extent33 p.en
dc.relation.ispartofseriesStatistics and Computingen
dc.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: [].en
dc.subjectPenalized empirical likelihooden
dc.subjectEmpirical likelihood ratioen
dc.titlePenalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariatesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.description.versionAccepted versionen
item.fulltextWith Fulltext-
Appears in Collections:SPMS Journal Articles

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.