Reproducible feature selection in high-dimensional accelerated failure time models

DOI10.1016/j.spl.2021.109275zbMath1478.62301OpenAlexW3206802674MaRDI QIDQ2070645

Daoji Li, Jia Zhou, Zemin Zheng, Yan Dong

Publication date: 24 January 2022

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2021.109275

zbMATH Keywords

high dimensionalityfalse discovery ratefeature selectionknockoffsaccelerated failure time models

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Testing in survival analysis and censored data (62N03) Reliability and life testing (62N05)

Uses Software

• glasso

Cites Work

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• Adapting to unknown sparsity by controlling the false discovery rate
• RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs
• Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates
• Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
• Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
• Independent Screening for Single-Index Hazard rate Models with Ultrahigh Dimensional Features
• The dantzig selector for censored linear regression models
• Adaptive Lasso for Cox's proportional hazards model

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