Adaptive estimation of the transition density of a Markov chain
In this paper a new estimator for the transition density $π$ 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 $π$ under mild conditions. The resulting estimate is adaptive with an optimal rate of convergence over a large range of anisotropic Besov spaces $B_{2,\infty}^{(α_1,α_2)}$. Some simulations are also presented.