[HTML][HTML] Rank-based variable selection with censored data
… In this article, we propose the ℓ 1 regularized Gehan estimator for simultaneous
estimation and variable selection which yields advantages in two fronts. First, the shrinkage
property of the ℓ 1 penalty and proper choice of tuning parameters build sparse models
without sacrificing accuracy. Secondly, the single criterion function with both components
being of ℓ 1 -type reduces (numerically) the minimization to a strictly linear programming
problem, making any … Additionally, we extend the ℓ 1 regularized procedure to …
estimation and variable selection which yields advantages in two fronts. First, the shrinkage
property of the ℓ 1 penalty and proper choice of tuning parameters build sparse models
without sacrificing accuracy. Secondly, the single criterion function with both components
being of ℓ 1 -type reduces (numerically) the minimization to a strictly linear programming
problem, making any … Additionally, we extend the ℓ 1 regularized procedure to …
Abstract
A rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable.
ncbi.nlm.nih.gov
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