Bayesian variable selection for survival data using inverse moment priors

DOI10.1214/20-AOAS1325zbMath1446.62284arXiv1712.02964OpenAlexW3102025474MaRDI QIDQ2194467

Amir Nikooienejad, Valen E. Johnson, Wen-Yi Wang

Publication date: 26 August 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1712.02964

zbMATH Keywords

high-dimensional datanonlocal priorBayesian variable selectioncancer genomicssurvival data analysisCox proportional hazard model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Statistical ranking and selection procedures (62F07) Reliability and life testing (62N05)

Related Items (7)

Model Uncertainty Quantification in Cox Regression ⋮ Two-Level Bayesian Interaction Analysis for Survival Data Incorporating Pathway Information ⋮ Bayesian Cox regression for large-scale inference with applications to electronic health records ⋮ The expectation-maximization approach for Bayesian additive Cox regression with current status data ⋮ Additive Bayesian variable selection under censoring and misspecification ⋮ An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models ⋮ A model selection approach for variable selection with censored data

Uses Software

• R
• L-BFGS
• glmnet
• BVSNLP
• c060
• dynpred

Cites Work

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