scholarly journals Nonparametric screening and feature selection for ultrahigh‐dimensional Case II interval‐censored failure time data

2020 ◽  
Vol 62 (8) ◽  
pp. 1909-1925
Author(s):  
Qiang Hu ◽  
Liang Zhu ◽  
Yanyan Liu ◽  
Jianguo Sun ◽  
Deo Kumar Srivastava ◽  
...  
Keyword(s):  
Feature Selection ◽  
Failure Time ◽  
Dimensional Case ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Selection For

Variable selection for interval-censored failure time data

2019 ◽  
Author(s):  
◽  
Qiwei Wu
Keyword(s):  
Variable Selection ◽  
Failure Time ◽  
Subset Selection ◽  
Failure Time Data ◽  
Time Data ◽  
University Of Missouri ◽  
Interval Censored ◽  
Selection For ◽  
The University

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Variable selection is a commonly asked question and various traditional variable selec- tion methods have been developed, including forward, backward and stepwise selec- tion, as well as best subset selection. Among these conventional selection techniques, the best subset selection is recommended by most researchers. However, this method requires fitting all sub-models, which can be very time-consuming when the number of covariates p is large.

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.

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