Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures

DOI10.3150/16-BEJ872zbMath1390.62008arXiv1406.4406MaRDI QIDQ2405180

Sophie Donnet, Judith Rousseau, Catia Scricciolo, Vincent Rivoirard

Publication date: 21 September 2017

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1406.4406

zbMATH Keywords

empirical Bayescounting processesDirichlet process mixturesAalen modelposterior contraction rates

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Empirical decision procedures; empirical Bayes procedures (62C12)

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Empirical Bayes Conditional Density Estimation ⋮ A deconvolution path for mixtures ⋮ Bayes and maximum likelihood for \(L^1\)-Wasserstein deconvolution of Laplace mixtures ⋮ Optimal convergence rates of Bayesian wavelet estimation with a novel empirical prior in nonparametric regression model ⋮ Approximation of Bayesian models for time-to-event data ⋮ Dirichlet process mixtures under affine transformations of the data ⋮ Oracle posterior contraction rates under hierarchical priors ⋮ Empirical priors and posterior concentration rates for a monotone density ⋮ Data-driven priors and their posterior concentration rates ⋮ Posterior contraction rates for deconvolution of Dirichlet-Laplace mixtures

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