The variable selection by the Dantzig selector for Cox’s proportional hazards model

2021 ◽  
Author(s):  
Kou Fujimori
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Dantzig Selector ◽  
Hazards Model ◽  
Cox's Proportional Hazards Model

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

scholarly journals Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ge-Jin Chu ◽  
Yong Liang ◽  
Jia-Xuan Wang
Keyword(s):  
Gene Expression ◽  
Variable Selection ◽  
Regularization Method ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
B Cell Lymphoma ◽  
Microarray Gene Expression ◽  
Hazards Model ◽  
Microarray Gene ◽  
Cox's Proportional Hazards Model

Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvexLq  (1/2<q<1)regularizations, to select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.

scholarly journals The Dantzig Selector in Cox's Proportional Hazards Model

2010 ◽  
Vol 37 (4) ◽  
pp. 531-552 ◽  
Author(s):  
ANESTIS ANTONIADIS ◽  
PIOTR FRYZLEWICZ ◽  
FRÉDÉRIQUE LETUÉ
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Dantzig Selector ◽  
Hazards Model ◽  
Cox's Proportional Hazards Model
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