Bayesian Variable Selections for Probit Models with Componentwise Gibbs Samplers

DOI10.1080/03610918.2014.922983zbMath1346.62050OpenAlexW2327890181MaRDI QIDQ2821041

Ray-Bing Chen, Yunchan Chi, Sheng-Mao Chang

Publication date: 16 September 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.922983

zbMATH Keywords

probit modelstochastic search variable selectionBayesian Lassobatch mean standard error

Mathematics Subject Classification ID

Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

Related Items (2)

Fast Bayesian variable screenings for binary response regressions with small sample size ⋮ Variational Bayesian inference for network autoregression models

Uses Software

• R

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Stochastic matching pursuit for Bayesian variable selection
• Secure Bayesian model averaging for horizontally partitioned data
• Markov chain Monte Carlo: can we trust the third significant figure?
• Optimal scaling for partially updating MCMC algorithms
• Nonparametric regression using Bayesian variable selection
• Calibration and empirical Bayes variable selection
• The Bayesian Lasso
• Bayesian lasso regression
• Flexible Empirical Bayes Estimation for Wavelets
• Bayesian Variable Selection and Regularization for Time–Frequency Surface Estimation
• Empirical Bayes Gibbs sampling
• A review of Bayesian variable selection methods: what, how and which

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