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

• المؤلفون الرئيسيون: Wang, Shanshan, Xiang, Liming
• مؤلفون آخرون: School of Physical and Mathematical Sciences
• التنسيق: مقال
• اللغة: English
• منشور في: 2016
• الموضوعات: Penalized empirical likelihood; Empirical likelihood ratio
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/83404; http://hdl.handle.net/10220/41436
• المؤسسة: 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...