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Group and within-group variable selection for competing risks data. Lifetime Data Anal 2018 Jul;24(3):407-424

Date

08/06/2017

Pubmed ID

28779228

Pubmed Central ID

PMC5797529

DOI

10.1007/s10985-017-9400-9

Scopus ID

2-s2.0-85026810429 (requires institutional sign-in at Scopus site)   10 Citations

Abstract

Variable selection in the presence of grouped variables is troublesome for competing risks data: while some recent methods deal with group selection only, simultaneous selection of both groups and within-group variables remains largely unexplored. In this context, we propose an adaptive group bridge method, enabling simultaneous selection both within and between groups, for competing risks data. The adaptive group bridge is applicable to independent and clustered data. It also allows the number of variables to diverge as the sample size increases. We show that our new method possesses excellent asymptotic properties, including variable selection consistency at group and within-group levels. We also show superior performance in simulated and real data sets over several competing approaches, including group bridge, adaptive group lasso, and AIC / BIC-based methods.

Author List

Ahn KW, Banerjee A, Sahr N, Kim S

Authors

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




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

Algorithms
Biomedical Research
Cluster Analysis
Models, Statistical
Proportional Hazards Models
Risk Assessment
Sample Size