Work

Variable selection in the joint frailty model of recurrent and terminal events using broken adaptive ridge regression

Year: 2026

Type: article

Abstract: We introduce a novel method to simultaneously perform variable selection and estimation in the joint frailty model of recurrent and terminal events using the broken adaptive ridge (BAR) regression penalty. The BAR penalty can be summarized as an iteratively reweighted squared L 2 -penalized regression, which approximates the L 0 -regularization method. Our method allows the number of covariates to diverge with the sample size. Under certain regularity conditions, we prove that the BAR estimator, implemented under the model framework, is consistent and asymptotically normally distributed, which are known as the oracle properties in the variable selection literature. In simulation studies, we compare our proposed method to the Minimum Information Criterion (MIC) method and other competitors. We apply our method to the Medical Information Mart for Intensive Care (MIMIC-III v1.4) database, with the aim of investigating which variables affect the risks of repeated hospitalization and death during hospitalization.

Source: Journal of Statistical Planning and Inference

DOI: 10.1016/j.jspi.2026.106422

Authors: Christian Chan, Fatemeh Mahmoudi, Chel Hee Lee, Quan Long, Xuewen Lu

Institutions: University of Calgary, Sunway University, Mount Royal University, Alberta Children's Hospital Research Institute, Hotchkiss Brain Institute

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,230