Medical College of Wisconsin
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Variable selection with group structure in competing risks quantile regression. Stat Med 2018 Apr 30;37(9):1577-1586

Date

02/23/2018

Pubmed ID

29468710

Pubmed Central ID

PMC5889760

DOI

10.1002/sim.7619

Scopus ID

2-s2.0-85042236376 (requires institutional sign-in at Scopus site)   6 Citations

Abstract

We study the group bridge and the adaptive group bridge penalties for competing risks quantile regression with group variables. While the group bridge consistently identifies nonzero group variables, the adaptive group bridge consistently selects variables not only at group level but also at within-group level. We allow the number of covariates to diverge as the sample size increases. The oracle property for both methods is also studied. The performance of the group bridge and the adaptive group bridge is compared in simulation and in a real data analysis. The simulation study shows that the adaptive group bridge selects nonzero within-group variables more consistently than the group bridge. A bone marrow transplant study is provided as an example.

Author List

Ahn KW, Kim S

Authors

Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of Wisconsin
Soyoung Kim PhD, BS, MS Associate Professor in the Data Science Institute department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Data Interpretation, Statistical
Humans
Models, Statistical
Regression Analysis
Risk Factors