Nonparametric estimation of the stationary density and the transition density of a Markov chain

Abstract

In this paper, we study first the problem of nonparametric estimation of the stationary density $f$ of a discrete-time Markov chain $(X_i)$. We consider a collection of projection estimators on finite dimensional linear spaces. We select an estimator among the collection by minimizing a penalized contrast. The same technique enables to estimate the density $g$ of $(X_i,X_{i+1})$ and so to provide an adaptive estimator of the transition density $\pi =g/f$. We give bounds in $L^2$ norm for these estimators and we show that they are adaptive in the minimax sense over a large class of Besov spaces. Some examples and simulations are also provided.

References in corpus (1)

• On the Poisson equation and diffusion approximation 3

Cited by in corpus (1)

• Penalized nonparametric mean square estimation of the coefficients of diffusion processes