Limit theorems for stationary Markov processes with L2-spectral gap

Abstract

Let $(X_t,Y_t{)}_{t\in T}$ be a discrete or continuous-time Markov process with state space $X\times R^d$ where $X$ is an arbitrary measurable set. Its transition semigroup is assumed to be additive with respect to the second component, i.e. $(X_t,Y_t{)}_{t\in T}$ is assumed to be a Markov additive process. In particular, this implies that the first component $(X_t{)}_{t\in T}$ is also a Markov process. Markov random walks or additive functionals of a Markov process are special instances of Markov additive processes. In this paper, the process $(Y_t{)}_{t\in T}$ is shown to satisfy the following classical limit theorems: (a) the central limit theorem, (b) the local limit theorem, (c) the one-dimensional Berry-Esseen theorem, (d) the one-dimensional first-order Edgeworth expansion, provided that we have sup{t\in(0,1]\cap T : E{π,0}[|Y_t| ^α] < 1 with the expected order with respect to the independent case (up to some $\epsilon >0$ for (c) and (d)). For the statements (b) and (d), a Markov nonlattice condition is also assumed as in the independent case. All the results are derived under the assumption that the Markov process $(X_t{)}_{t\in T}$ has an invariant probability distribution $\pi$, is stationary and has the $L^2(\pi )$-spectral gap property (that is, $(X_t)t\in N}$ is $\rho$-mixing in the discrete-time case). The case where $(X_t{)}_{t\in T}$ is non-stationary is briefly discussed. As an application, we derive a Berry-Esseen bound for the M-estimators associated with $\rho$-mixing Markov chains.

35 pages Accepted(6 january 2011) for publication in Annales de l'Institut Henri Poincare - Probabilites et Statistiques

References in corpus (5)

• Limit theorems for additive functionals of a Markov chain
• Lower estimates of transition densities and bounds on exponential ergodicity for stochastic PDE's
• A uniform Berry--Esseen theorem on $M$-estimators for geometrically ergodic Markov chains
• Vitesse de convergence dans le théorème limite central pour des chaînes de Markov fortement ergodiques
• Characterization of weak convergence of Birkhoff sums for Gibbs-Markov maps

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