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

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

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...