Adaptive estimation of the transition density of a particular hidden Markov chain

We study the following model of hidden Markov chain: with (X_i) a real-valued positive recurrent and stationary Markov chain, and (?_i)_1?i?n+1 a noise independent of the sequence (X_i) having a known distribution. We present an adaptive estimator of the transition density based on the quotient of a deconvolution estimator of the density of X_i and an estimator of the density of (X_i,X_i+1). These estimators are obtained by contrast minimization and model selection. We evaluate the L² risk and its rate of convergence for ordinary smooth and supersmooth noise with regard to ordinary smooth and supersmooth chains. Some examples are also detailed.