An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models

DOI10.1007/S11222-020-09974-2zbMath1461.62024OpenAlexW3118851742MaRDI QIDQ2029084

Kitty Yuen Yi Wan, Jim E. Griffin

Publication date: 3 June 2021

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-020-09974-2

zbMATH Keywords

Laplace approximationdata augmentationhigh-dimensional regressiongene expressioncorrelated pseudo-marginal methodPólya-gamma sampling

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Reliability and life testing (62N05)

Related Items (1)

Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection

Uses Software

• BVSNLP
• EMVS

Cites Work

• Unnamed Item
• Unnamed Item
• The pseudo-marginal approach for efficient Monte Carlo computations
• The Correlated Pseudo-Marginal Method
• The Pólya-gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic
• On the computational complexity of high-dimensional Bayesian variable selection
• Sequential Monte Carlo on large binary sampling spaces
• Bayesian variable selection for survival data using inverse moment priors
• Mixtures ofg-Priors in Generalized Linear Models
• Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage
• Mixtures of g Priors for Bayesian Variable Selection
• The Calculation of Posterior Distributions by Data Augmentation
• Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings
• On Sampling Strategies in Bayesian Variable Selection Problems With Large Model Spaces
• EMVS: The EM Approach to Bayesian Variable Selection
• Scalable Importance Tempering and Bayesian Variable Selection
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
• Monte Carlo strategies in scientific computing
• A review of Bayesian variable selection methods: what, how and which
• Bayesian auxiliary variable models for binary and multinomial regression

This page was built for publication: An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models