Broken adaptive ridge regression for right-censored survival data

Zhihua Sun (), Yi Liu (), Kani Chen () and Gang Li ()
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Zhihua Sun: Ocean University of China
Yi Liu: Ocean University of China
Kani Chen: Hong Kong University of Science and Technology
Gang Li: University of California

Annals of the Institute of Statistical Mathematics, 2022, vol. 74, issue 1, No 4, 69-91

Abstract: Abstract Broken adaptive ridge (BAR) is a computationally scalable surrogate to $$L_0$$ L 0 -penalized regression, which involves iteratively performing reweighted $$L_2$$ L 2 penalized regressions and enjoys some appealing properties of both $$L_0$$ L 0 and $$L_2$$ L 2 penalized regressions while avoiding some of their limitations. In this paper, we extend the BAR method to the semi-parametric accelerated failure time (AFT) model for right-censored survival data. Specifically, we propose a censored BAR (CBAR) estimator by applying the BAR algorithm to the Leurgan’s synthetic data and show that the resulting CBAR estimator is consistent for variable selection, possesses an oracle property for parameter estimation and enjoys a grouping property for highly correlation covariates. Both low- and high-dimensional covariates are considered. The effectiveness of our method is demonstrated and compared with some popular penalization methods using simulations. Real data illustrations are provided on a diffuse large-B-cell lymphoma data and a glioblastoma multiforme data.

Keywords: Accelerated failure time model; Grouping effect; $$L_0$$ L 0 penalization; Right censoring; Variable selection (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10463-021-00794-3

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