Variable selection for high-dimensional genomic data with censored outcomes using group Lasso prior

DOI10.1016/j.csda.2017.02.014zbMath1464.62115OpenAlexW2590149070MaRDI QIDQ1654247

Y. Aharonov

Publication date: 7 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2017.02.014

zbMATH Keywords

Gibbs samplergroup Lassopenalized regressionaccelerated failure time modelBayesian Lasso

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Reliability and life testing (62N05)

Related Items

Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models ⋮ Identification of survival relevant genes with measurement error in gene expression incorporated ⋮ Bi-level feature selection in high dimensional AFT models with applications to a genomic study ⋮ psbcGroup

Uses Software

• R
• BayesDA
• AS 136
• uniCox

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