Name
Abstract | ||
|---|---|---|
Nowadays, a series of methods are based on a L1 penalty to solve the variable selection problem for a Cox's proportional hazards model. In 2010, Xu et al. have proposed a L1/2 regularization and proved that the L1/2 penalty is sparser than the L1 penalty in linear regression models. In this paper, we propose a novel shooting method for the L1/2 regularization and apply it on the Cox model for variable selection. The experimental results based on comprehensive simulation studies, real Primary Biliary Cirrhosis and diffuse large B cell lymphoma datasets show that the L1/2 regularization shooting method performs competitively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s00500-013-1042-6 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
cox model,l1/2 regularization shooting algorithm,lasso,variable selection | Shooting method,Mathematical optimization,Proportional hazards model,Feature selection,Lasso (statistics),Regularization (mathematics),Mathematics,Linear regression | Journal |
Volume | Issue | ISSN |
18 | 1 | 14337479 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin-Ze Luan | 1 | 29 | 2.31 |
| Yong Liang | 2 | 180 | 24.35 |
| Cheng Liu | 3 | 33 | 3.38 |
| Kwong-Sak Leung | 4 | 1887 | 205.58 |
| Tak-ming Chan | 5 | 190 | 13.57 |
| Zongben Xu | 6 | 3203 | 198.88 |
| Hai Zhang | 7 | 92 | 12.83 |