Up to now, the nonparametric analysis of multidimensional continuous-time Markov
processes has focussed strongly on specific model choices, mostly related to symmetry of …

Up to now, the nonparametric analysis of multidimensional continuous-time Markov
processes has focussed strongly on specific model choices, mostly related to symmetry of …

We derive uniform concentration inequalities for continuous-time analogues of empirical
processes and related stochastic integrals of scalar ergodic diffusion processes. Thereby …

In this paper, we study the problem of non parametric estimation of the stationary marginal
density f of an α or a β-mixing process, observed either in continuous time or in discrete time …

We consider the solution X=(X t) t≥ 0 of a multivariate stochastic differential equation with
Levy-type jumps and with unique invariant probability measure with density μ. We assume …

This paper is devoted to the estimation of the common marginal density function of weakly
dependent processes. The accuracy of estimation is measured using pointwise risks. We …

It is shown that a simple Dirichlet process mixture of multivariate normals offers Bayesian
density estimation with adaptive posterior convergence rates. Toward this, a novel sieve for …

Density estimation represents one of the most successful applications of Bayesian
Nonparametrics. In particular, Dirichlet process mixtures of normals are the gold standard for …

We study the Lπ− integrated risk with π≥ 2 of an adaptive density estimator by wavelets
method for absolutely regular observations. By a duality argument, the study of the risk is …

This work is an overview of statistical inference in stationary, discrete time stochastic
processes. Results in the last fifteen years, particularly on non-Gaussian sequences and …