Group and within-group variable selection for competing risks data

Kwang Woo Ahn (), Anjishnu Banerjee, Natasha Sahr and Soyoung Kim
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Kwang Woo Ahn: Medical College of Wisconsin
Anjishnu Banerjee: Medical College of Wisconsin
Natasha Sahr: Medical College of Wisconsin
Soyoung Kim: Medical College of Wisconsin

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 3, No 2, 407-424

Abstract: 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.

Keywords: Adaptive penalty; Clustered data; Competing risks data; Group bridge (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9400-9

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