Rank-based estimation in the ℓ1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data

DOI10.1093/biostatistics/kxp020zbMath1437.62505OpenAlexW2134385763WikidataQ33474260 ScholiaQ33474260MaRDI QIDQ3304984

Brent A. Johnson

Publication date: 4 August 2020

Published in: Biostatistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biostatistics/kxp020

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

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Uses Software

• UCI-ml

Cites Work

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• Boosting algorithms: regularization, prediction and model fitting
• Estimating regression parameters using linear rank tests for censored data
• Asymptotics for Lasso-type estimators.
• Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates
• Variable Selection in Semiparametric Linear Regression with Censored Data
• Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
• Regularized Estimation for the Accelerated Failure Time Model
• Linear rank tests with right censored data
• Rank-based inference for the accelerated failure time model
• A generalized Wilcoxon test for comparing arbitrarily singly-censored samples

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