On posterior concentration in misspecified models

DOI10.1214/15-BA941zbMath1335.62022arXiv1312.4620MaRDI QIDQ273635

Karthik Sriram, Ryan Martin, R. V. Ramamoorthi

Publication date: 22 April 2016

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.4620

zbMATH Keywords

consistencyBayesianKullback-Leiblermisspecified

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Bayesian problems; characterization of Bayes procedures (62C10) Strong limit theorems (60F15) Statistical aspects of information-theoretic topics (62B10)

Related Items (10)

On Bayesian quantile regression using a pseudo-joint asymmetric Laplace likelihood ⋮ General Robust Bayes Pseudo-Posteriors: Exponential Convergence Results with Applications ⋮ Gibbs posterior inference on the minimum clinically important difference ⋮ A comparison of learning rate selection methods in generalized Bayesian inference ⋮ Gibbs posterior concentration rates under sub-exponential type losses ⋮ A review of Bayesian asymptotics in general insurance applications ⋮ Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors ⋮ Bayesian fractional posteriors ⋮ Posterior concentration for a misspecified Bayesian regression model with functional covariates ⋮ Robust sparse Bayesian infinite factor models

Cites Work

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• Bayesian nonparametrics
• Dynamics of Bayesian updating with dependent data and misspecified models
• Misspecification in infinite-dimensional Bayesian statistics
• Limiting Behavior of Posterior Distributions when the Model is Incorrect
• Bayesian quantile regression

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