Adaptive estimation of the transition density of a Markov chain

Abstract

In this paper a new estimator for the transition density $\pi$ of an homogeneous Markov chain is considered. We introduce an original contrast derived from regression framework and we use a model selection method to estimate $\pi$ under mild conditions. The resulting estimate is adaptive with an optimal rate of convergence over a large range of anisotropic Besov spaces $B_{2,\infty }^{({\alpha }_1,{\alpha }_2)}$. Some simulations are also presented.

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