Donsker and Glivenko-Cantelli theorems for a class of processes generalizing the empirical process
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Publication:470509
DOI10.1214/14-EJS955zbMath1320.60092OpenAlexW4297914891MaRDI QIDQ470509
Publication date: 12 November 2014
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ejs/1415023526
empirical processesrandom measuresDonsker theoremGlivenko-Cantelli theoremnonparametric Bayesian theory
Random measures (60G57) Functional limit theorems; invariance principles (60F17) Nonparametric inference (62G99)
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Cites Work
- On the Bernstein-von Mises phenomenon for nonparametric Bayes procedures
- Central limit theorems for stochastic processes under random entropy conditions
- On the Gaussian approximation of convolutions under multidimensional analogues of S. N. Bernstein's inequality conditions
- Uniform Donsker classes of functions
- Exchangeably weighted bootstraps of the general empirical process
- Weak convergence and empirical processes. With applications to statistics
- A Bayesian analysis of some nonparametric problems
- An asymptotic analysis of a class of discrete nonparametric priors
- Posterior Analysis for Normalized Random Measures with Independent Increments
- Bayesian Nonparametrics
- Probability with Martingales
- Uniform Central Limit Theorems
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