Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

SICA for Cox’s proportional hazards model with a diverging number of parameters

SICA for Cox’s proportional hazards model with a diverging number of parameters The smooth integration of counting and absolute deviation (SICA) penalized variable selection procedure for high-dimensional linear regression models is proposed by Lv and Fan (2009). In this article, we extend their idea to Cox’s proportional hazards (PH) model by using a penalized log partial likelihood with the SICA penalty. The number of the regression coefficients is allowed to grow with the sample size. Based on an approximation to the inverse of the Hessian matrix, the proposed method can be easily carried out with the smoothing quasi-Newton (SQN) algorithm. Under appropriate sparsity conditions, we show that the resulting estimator of the regression coefficients possesses the oracle property. We perform an extensive simulation study to compare our approach with other methods and illustrate it on a well known PBC data for predicting survival from risk factors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

SICA for Cox’s proportional hazards model with a diverging number of parameters

Download PDF
16 pages

Loading next page...
 
/lp/springer-journals/sica-for-cox-s-proportional-hazards-model-with-a-diverging-number-of-y5ysR0INPG

References (42)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/s10255-014-0402-z
Publisher site
See Article on Publisher Site

Abstract

The smooth integration of counting and absolute deviation (SICA) penalized variable selection procedure for high-dimensional linear regression models is proposed by Lv and Fan (2009). In this article, we extend their idea to Cox’s proportional hazards (PH) model by using a penalized log partial likelihood with the SICA penalty. The number of the regression coefficients is allowed to grow with the sample size. Based on an approximation to the inverse of the Hessian matrix, the proposed method can be easily carried out with the smoothing quasi-Newton (SQN) algorithm. Under appropriate sparsity conditions, we show that the resulting estimator of the regression coefficients possesses the oracle property. We perform an extensive simulation study to compare our approach with other methods and illustrate it on a well known PBC data for predicting survival from risk factors.

Journal

Acta Mathematicae Applicatae Sinica – Springer Journals

Published: Nov 6, 2014

There are no references for this article.