Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates

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 app...

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Bibliographic Details
Main Authors: Wang, Shanshan, Xiang, Liming
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2016
Subjects:
Online Access:https://hdl.handle.net/10356/83404
http://hdl.handle.net/10220/41436
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Institution: Nanyang Technological University
Language: English