27 citations · 51 across the 12 of their papers we have counts for
7 papers · 1 filter
Wasserstein convergence in Bayesian and frequentist deconvolution models
Judith Rousseau, Catia Scricciolo
We study the multivariate deconvolution problem of recovering the distribution of a signal from independent and identically distributed observations additively contaminated with ra…
Wasserstein convergence in Bayesian deconvolution models
Judith Rousseau, Catia Scricciolo
We study the reknown deconvolution problem of recovering a distribution function from independent replicates (signal) additively contaminated with random errors (noise), whose dist…
On asymptotically efficient maximum likelihood estimation of linear functionals in Laplace measurement error models
Catia Scricciolo
Maximum likelihood estimation of linear functionals in the inverse problem of deconvolution is considered. Given observations of a random sample from a distribution $P_0\equiv P_{F…
Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures
Sophie Donnet, Vincent Rivoirard, Judith Rousseau +1
In this paper we provide general conditions to check on the model and the prior to derive posterior concentration rates for data-dependent priors (or empirical Bayes approaches). W…
Adaptive Bayesian density estimation using Pitman-Yor or normalized inverse-Gaussian process kernel mixtures
Catia Scricciolo
We consider Bayesian nonparametric density estimation using a Pitman-Yor or a normalized inverse-Gaussian process kernel mixture as the prior distribution for a density. The proced…
Bayes and empirical Bayes: do they merge?
Sonia Petrone, Judith Rousseau, Catia Scricciolo
Bayesian inference is attractive for its coherence and good frequentist properties. However, it is a common experience that eliciting a honest prior may be difficult and, in practi…