Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis

DOI10.1214/11-AOS913zbMath1231.62008arXiv1202.5160MaRDI QIDQ661177

Hani Doss, Eugenia Buta

Publication date: 21 February 2012

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

Full work available at URL: https://arxiv.org/abs/1202.5160

zbMATH Keywords

importance samplingMarkov chain Monte CarloergodicityBayes factorscontrol variateshyperparameter selection

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Point estimation (62F10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (5)

Efficient estimation of the link function parameter in a robust Bayesian binary regression model ⋮ An MCMC approach to empirical Bayes inference and Bayesian sensitivity analysis via empirical processes ⋮ Selection of Proposal Distributions for Multiple Importance Sampling ⋮ Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis ⋮ Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions

Uses Software

• tsbridge

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis
• Empirical distributions in selection bias models
• Large sample theory of empirical distributions in biased sampling models
• Nonparametric regression using Bayesian variable selection
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Markov chains for exploring posterior distributions. (With discussion)
• On the convergence of the Markov chain simulation method
• Batch means and spectral variance estimators in Markov chain Monte Carlo
• Empirical Bayes vs. fully Bayes variable selection
• Calibration and empirical Bayes variable selection
• Mixtures of g Priors for Bayesian Variable Selection
• On a Likelihood Approach for Monte Carlo Integration
• A Theory of Statistical Models for Monte Carlo Integration
• Regeneration in Markov Chain Samplers
• Monte Carlo sampling methods using Markov chains and their applications
• A Bayesian Semiparametric Model for Random-Effects Meta-Analysis
• Benchmark priors for Bayesian model averaging.

This page was built for publication: Computational approaches for empirical Bayes methods and Bayesian sensitivity analysis