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November 2019 Uniform rates of the Glivenko–Cantelli convergence and their use in approximating Bayesian inferences
Emanuele Dolera, Eugenio Regazzini
Bernoulli 25(4A): 2982-3015 (November 2019). DOI: 10.3150/18-BEJ1077

Abstract

This paper deals with suitable quantifications in approximating a probability measure by an “empirical” random probability measure $\hat{\mathfrak{p}}_{n}$, depending on the first $n$ terms of a sequence $\{\tilde{\xi}_{i}\}_{i\geq1}$ of random elements. Section 2 studies the range of oscillation near zero of the Wasserstein distance $\mathrm{d}^{(p)}_{[\mathbb{S}]}$ between $\mathfrak{p}_{0}$ and $\hat{\mathfrak{p}}_{n}$, assuming the $\tilde{\xi}_{i}$’s i.i.d. from $\mathfrak{p}_{0}$. In Theorem 2.1 $\mathfrak{p}_{0}$ can be fixed in the space of all probability measures on $(\mathbb{R}^{d},\mathscr{B}(\mathbb{R}^{d}))$ and $\hat{\mathfrak{p}}_{n}$ coincides with the empirical measure $\tilde{\mathfrak{e}}_{n}:=\frac{1}{n}\sum_{i=1}^{n}\delta_{\tilde{\xi}_{i}}$. In Theorem 2.2 (Theorem 2.3, respectively), $\mathfrak{p}_{0}$ is a $d$-dimensional Gaussian distribution (an element of a distinguished statistical exponential family, respectively) and $\hat{\mathfrak{p}}_{n}$ is another $d$-dimensional Gaussian distribution with estimated mean and covariance matrix (another element of the same family with an estimated parameter, respectively). These new results improve on allied recent works by providing also uniform bounds with respect to $n$, meaning the finiteness of the $p$-moment of $\mathop{\mathrm{sup}}_{n\geq1}b_{n}\mathrm{d}^{(p)}_{[\mathbb{S}]}(\mathfrak{p}_{0},\hat{\mathfrak{p}}_{n})$ is proved for some diverging sequence $b_{n}$ of positive numbers. In Section 3, assuming the $\tilde{\xi}_{i}$’s exchangeable, one studies the range of oscillation near zero of the Wasserstein distance between the conditional distribution – also called posterior – of the directing measure of the sequence, given $\tilde{\xi}_{1},\ldots,\tilde{\xi}_{n}$, and the point mass at $\hat{\mathfrak{p}}_{n}$. Similarly, a bound for the approximation of predictive distributions is given. Finally, Theorems from 3.3 to 3.5 reconsider Theorems from 2.1 to 2.3, respectively, according to a Bayesian perspective.

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Emanuele Dolera. Eugenio Regazzini. "Uniform rates of the Glivenko–Cantelli convergence and their use in approximating Bayesian inferences." Bernoulli 25 (4A) 2982 - 3015, November 2019. https://doi.org/10.3150/18-BEJ1077

Information

Received: 1 December 2017; Revised: 1 July 2018; Published: November 2019
First available in Project Euclid: 13 September 2019

zbMATH: 07110118
MathSciNet: MR4003571
Digital Object Identifier: 10.3150/18-BEJ1077

Rights: Copyright © 2019 Bernoulli Society for Mathematical Statistics and Probability

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Vol.25 • No. 4A • November 2019
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