Electronic Journal of Statistics

The Bernstein-Von-Mises theorem under misspecification

B.J.K. Kleijn and A.W. van der Vaart

Full-text: Open access

Abstract

We prove that the posterior distribution of a parameter in misspecified LAN parametric models can be approximated by a random normal distribution. We derive from this that Bayesian credible sets are not valid confidence sets if the model is misspecified. We obtain the result under conditions that are comparable to those in the well-specified situation: uniform testability against fixed alternatives and sufficient prior mass in neighbourhoods of the point of convergence. The rate of convergence is considered in detail, with special attention for the existence and construction of suitable test sequences. We also give a lemma to exclude testable model subsets which implies a misspecified version of Schwartz’ consistency theorem, establishing weak convergence of the posterior to a measure degenerate at the point at minimal Kullback-Leibler divergence with respect to the true distribution.

Article information

Source
Electron. J. Statist., Volume 6 (2012), 354-381.

Dates
First available in Project Euclid: 19 March 2012

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1332162333

Digital Object Identifier
doi:10.1214/12-EJS675

Mathematical Reviews number (MathSciNet)
MR2988412

Zentralblatt MATH identifier
1274.62203

Subjects
Primary: 62F15: Bayesian inference 62F25: Tolerance and confidence regions 62F12: Asymptotic properties of estimators

Keywords
Misspecification posterior distribution credible set limit distribution rate of convergence consistency

Citation

Kleijn, B.J.K.; van der Vaart, A.W. The Bernstein-Von-Mises theorem under misspecification. Electron. J. Statist. 6 (2012), 354--381. doi:10.1214/12-EJS675. https://projecteuclid.org/euclid.ejs/1332162333


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