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

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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書目詳細資料
Main Authors:	Wang, Shanshan, Xiang, Liming
其他作者:	School of Physical and Mathematical Sciences
格式:	Article
語言:	English
出版:	2016
主題:	Penalized empirical likelihood Empirical likelihood ratio
在線閱讀:	https://hdl.handle.net/10356/83404 http://hdl.handle.net/10220/41436
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

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