Estimation of the transition density of a Markov chain

TL;DR

This paper introduces two data-driven methods to estimate the transition density of a homogeneous Markov chain, providing risk bounds and convergence rates under various Besov space conditions.

Contribution

It proposes novel estimation procedures with theoretical guarantees for transition densities in Markov chains, including risk bounds and model selection theorems.

Findings

Non-asymptotic risk bounds for the estimators

Convergence rates over Besov spaces

Simulation results demonstrating estimator performance

Abstract

We present two data-driven procedures to estimate the transition density of an homogeneous Markov chain. The first yields to a piecewise constant estimator on a suitable random partition. By using an Hellinger-type loss, we establish non-asymptotic risk bounds for our estimator when the square root of the transition density belongs to possibly inhomogeneous Besov spaces with possibly small regularity index. Some simulations are also provided. The second procedure is of theoretical interest and leads to a general model selection theorem from which we derive rates of convergence over a very wide range of possibly inhomogeneous and anisotropic Besov spaces. We also investigate the rates that can be achieved under structural assumptions on the transition density.