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2021 Posterior asymptotics in Wasserstein metrics on the real line
Minwoo Chae, Pierpaolo De Blasi, Stephen G. Walker
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Electron. J. Statist. 15(2): 3635-3677 (2021). DOI: 10.1214/21-EJS1869

Abstract

In this paper, we use the class of Wasserstein metrics to study asymptotic properties of posterior distributions. Our first goal is to provide sufficient conditions for posterior consistency. In addition to the well-known Schwartz’s Kullback–Leibler condition on the prior, the true distribution and most probability measures in the support of the prior are required to possess moments up to an order which is determined by the order of the Wasserstein metric. We further investigate convergence rates of the posterior distributions for which we need stronger moment conditions. The required tail conditions are sharp in the sense that the posterior distribution may be inconsistent or contract slowly to the true distribution without these conditions. Our study involves techniques that build on recent advances on Wasserstein convergence of empirical measures. We apply the results to some examples including a Dirichlet process mixture prior and conduct a simulation study for further illustration.

Funding Statement

M. Chae was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1A01054718). P. De Blasi is supported by MIUR, PRIN Project 2015SNS29B and acknowledges “Dipartimenti di Eccellenza” Grant 2018-2020.

Acknowledgments

The authors are grateful for the comments of reviewers on an earlier version of the paper.

Citation

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Minwoo Chae. Pierpaolo De Blasi. Stephen G. Walker. "Posterior asymptotics in Wasserstein metrics on the real line." Electron. J. Statist. 15 (2) 3635 - 3677, 2021. https://doi.org/10.1214/21-EJS1869

Information

Received: 1 August 2020; Published: 2021
First available in Project Euclid: 15 July 2021

Digital Object Identifier: 10.1214/21-EJS1869

Subjects:
Primary: 62F15 , 62G20
Secondary: 62G07

Keywords: Dirichlet process mixture , nonparametric Bayesian inference , posterior consistency , posterior convergence rate , Wasserstein metrics

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Vol.15 • No. 2 • 2021
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