  We observe n possibly dependent random variables, the distribution of which
is presumed to be stationary even though this might not be true, and we aim at
estimating the stationary distribution. We establish a non-asymptotic deviation
bound for the Hellinger distance between the target distribution and our
estimator. If the dependence within the observations is small, the estimator
performs as good as if the data were independent and identically distributed.
In addition our estimator is robust to misspecification and contamination. If
the dependence is too high but the observed process is mixing, we can select a
subset of observations that is almost independent and retrieve results similar
to what we have in the i.i.d. case. We apply our procedure to the estimation of
the invariant distribution of a diffusion process and to finite state space
hidden Markov models.
