Estimation and variable selection via frailty models with penalized likelihood

2012 ◽  
Vol 31 (20) ◽  
pp. 2223-2239 ◽  
Author(s):  
E. Androulakis ◽  
C. Koukouvinos ◽  
F. Vonta
Keyword(s):  
Variable Selection ◽  
Penalized Likelihood ◽  
Frailty Models

scholarly journals Variable selection in subdistribution hazard frailty models with competing risks data

2014 ◽  
Vol 33 (26) ◽  
pp. 4590-4604 ◽  
Author(s):  
Il Do Ha ◽  
Minjung Lee ◽  
Seungyoung Oh ◽  
Jong-Hyeon Jeong ◽  
Richard Sylvester ◽  
...  
Keyword(s):  
Variable Selection ◽  
Competing Risks ◽  
Frailty Models ◽  
Subdistribution Hazard ◽  
Competing Risks Data

Variable Selection in General Frailty Models Using Penalized H-Likelihood

2014 ◽  
Vol 23 (4) ◽  
pp. 1044-1060 ◽  
Author(s):  
Il Do Ha ◽  
Jianxin Pan ◽  
Seungyoung Oh ◽  
Youngjo Lee
Keyword(s):  
Variable Selection ◽  
Frailty Models

scholarly journals A mixture of local and quadratic approximation variable selection algorithm in nonconcave penalized regression

2013 ◽  
Vol Volume 16, 2012 ◽  
Author(s):  
Assi N'GUESSAN ◽  
Ibrahim Sidi Zakari ◽  
Assi Mkhadri
Keyword(s):  
Variable Selection ◽  
Penalized Likelihood ◽  
Real Data ◽  
Penalized Regression ◽  
Penalty Functions ◽  
Selection Algorithm ◽  
Data Set ◽  
International Audience ◽  
One Step ◽  
Nonconvex Penalty

International audience We consider the problem of variable selection via penalized likelihood using nonconvex penalty functions. To maximize the non-differentiable and nonconcave objective function, an algorithm based on local linear approximation and which adopts a naturally sparse representation was recently proposed. However, although it has promising theoretical properties, it inherits some drawbacks of Lasso in high dimensional setting. To overcome these drawbacks, we propose an algorithm (MLLQA) for maximizing the penalized likelihood for a large class of nonconvex penalty functions. The convergence property of MLLQA and oracle property of one-step MLLQA estimator are established. Some simulations and application to a real data set are also presented.

scholarly journals Variable selection in joint frailty models of recurrent and terminal events

Biometrics ◽  
2020 ◽  
Vol 76 (4) ◽  
pp. 1330-1339
Author(s):  
Dongxiao Han ◽  
Xiaogang Su ◽  
Liuquan Sun ◽  
Zhou Zhang ◽  
Lei Liu
Keyword(s):  
Variable Selection ◽  
Frailty Models ◽  
Terminal Events

scholarly journals Variable Selection in Frailty Models using FrailtyHL R Package: Breast Cancer Survival Data

2015 ◽  
Vol 28 (5) ◽  
pp. 965-976 ◽  
Author(s):  
Bohyeon Kim ◽  
Il Do Ha ◽  
Maengseok Noh ◽  
Myung Hwan Na ◽  
Ho-Chun Song ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Variable Selection ◽  
Survival Data ◽  
Cancer Survival ◽  
R Package ◽  
Breast Cancer Survival ◽  
Frailty Models ◽  
Cancer Survival Data

scholarly journals Erratum to: Variable Selection in Joint Mean and Dispersion Models via Double Penalized Likelihood

Sankhya B ◽  
2014 ◽  
Vol 76 (2) ◽  
pp. 335-335
Author(s):  
Christiana Charalambous ◽  
Jianxin Pan ◽  
Mark Tranmer
Keyword(s):  
Variable Selection ◽  
Penalized Likelihood ◽  
Dispersion Models
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