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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Main Authors: | Wang, Shanshan, Xiang, Liming |
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Other Authors: | School of Physical and Mathematical Sciences |
Format: | Article |
Language: | English |
Published: |
2016
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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 |
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