Spectral thresholding for the estimation of Markov chain transition operators

Abstract

We consider nonparametric estimation of the transition operator P of a Markov chain and its transition density p where the singular values of P are assumed to decay exponentially fast. This is for instance the case for periodised, reversible multi-dimensional diffusion processes observed in low frequency.

We investigate the performance of a spectral hard thresholded Galerkin-type estimator for P and p, discarding most of the estimated singular triplets. The construction is based on smooth basis functions such as wavelets or B-splines. We show its statistical optimality by establishing matching minimax upper and lower bounds in $L^2$-loss. Particularly, the effect of the dimensionality d of the state space on the nonparametric rate improves from $2d$ to d compared to the case without singular value decay.