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Regularized Estimation in the Accelerated Failure Time Model with High‐Dimensional Covariates
Author(s) -
Huang Jian,
Ma Shuangge,
Xie Huiliang
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00562.x
Subject(s) - akaike information criterion , covariate , estimator , accelerated failure time model , censoring (clinical trials) , mathematics , statistics , model selection , lasso (programming language) , ordinary least squares , least squares function approximation , generalized least squares , regularization (linguistics) , feature selection , computer science , artificial intelligence , world wide web
Summary We consider two regularization approaches, the LASSO and the threshold‐gradient‐directed regularization, for estimation and variable selection in the accelerated failure time model with multiple covariates based on Stute's weighted least squares method. The Stute estimator uses Kaplan–Meier weights to account for censoring in the least squares criterion. The weighted least squares objective function makes the adaptation of this approach to multiple covariate settings computationally feasible. We use V ‐fold cross‐validation and a modified Akaike's Information Criterion for tuning parameter selection, and a bootstrap approach for variance estimation. The proposed method is evaluated using simulations and demonstrated on a real data example.