Spectral analysis of Markov kernels and application to the convergence rate of discrete random walks

Let $\{X_n\}_{n\in\N}$ be a Markov chain on a measurable space \X with transition kernel $P$ and let V:\X\r[1,+\infty). The Markov kernel $P$ is here considered as a linear bounded operator on the weighted-supremum space \cB_V associated with $V$. Then the combination of quasi-compactness arguments with precise analysis of eigen-elements of $P$ allows us to estimate the geometric rate of convergence $\rho_V(P)$ of $\{X_n\}_{n\in\N}$ to its invariant probability measure in operator norm on \cB_V. A general procedure to compute $\rho_V(P)$ for discrete Markov random walks with identically distributed bounded increments is specified

Multiplicative ergodicity of Laplace transforms for additive functional of Markov chains

We study properties of the Laplace transforms of non-negative additive functionals of Markov chains. We are namely interested in a multiplicative ergodicity property used in [18] to study bifurcating processes with ancestral dependence. We develop a general approach based on the use of the operator perturbation method. We apply our general results to two examples of Markov chains, including a linear autoregressive model. In these two examples the operator-type assumptions reduce to some expected finite moment conditions on the functional (no exponential moment conditions are assumed in this work)

A Renewal Theorem for Strongly Ergodic Markov Chains in Dimension d≥3d\geq3 and Centered Case

In dimension $d\geq3$, we present a general assumption under which the renewal theorem established by Spitzer for i.i.d. sequences of centered nonlattice r.v. holds true. Next we appeal to an operator-type procedure to investigate the Markov case. Such a spectral approach has been already developed by Babillot, but the weak perturbation theorem of Keller and Liverani enables us to greatly weaken thehypotheses in terms of moment conditions. Our applications concern the v$-geometrically ergodic Markov chains, the$\rho$-mixing Markov chains, and the iterative Lipschitz models, for which the renewal theorem of the i.i.d. case extends under the (almost) expected moment condition

Additional material on bounds of ℓ2\ell^2-spectral gap for discrete Markov chains with band transition matrices

We analyse the $\ell^2(\pi)$-convergence rate of irreducible and aperiodic Markov chains with $N$-band transition probability matrix $P$ and with invariant distribution $\pi$. This analysis is heavily based on: first the study of the essential spectral radius $r\_{ess}(P\_{|\ell^2(\pi)})$ of $P\_{|\ell^2(\pi)}$ derived from Hennion's quasi-compactness criteria; second the connection between the spectral gap property (SG$\_2$) of $P$ on $\ell^2(\pi)$ and the $V$-geometric ergodicity of $P$. Specifically, (SG$\_2$) is shown to hold under the condition \alpha\_0 := \sum\_{{m}=-N}^N \limsup\_{i\rightarrow +\infty} \sqrt{P(i,i+{m})\, P^*(i+{m},i)}\ \textless{}\, 1. Moreover $r\_{ess}(P\_{|\ell^2(\pi)}) \leq \alpha\_0$. Simple conditions on asymptotic properties of $P$ and of its invariant probability distribution $\pi$ to ensure that \alpha\_0\textless{}1 are given. In particular this allows us to obtain estimates of the $\ell^2(\pi)$-geometric convergence rate of random walks with bounded increments. The specific case of reversible $P$ is also addressed. Numerical bounds on the convergence rate can be provided via a truncation procedure. This is illustrated on the Metropolis-Hastings algorithm

The Nagaev-Guivarc'h method via the Keller-Liverani theorem

The Nagaev-Guivarc'h method, via the perturbation operator theorem of Keller and Liverani, has been exploited in recent papers to establish local limit and Berry-Essen type theorems for unbounded functionals of strongly ergodic Markov chains. The main difficulty of this approach is to prove Taylor expansions for the dominating eigenvalue of the Fourier kernels. This paper outlines this method and extends it by proving a multi-dimensional local limit theorem, a first-order Edgeworth expansion, and a multi-dimensional Berry-Esseen type theorem in the sense of Prohorov metric. When applied to uniformly or geometrically ergodic chains and to iterative Lipschitz models, the above cited limit theorems hold under moment conditions similar, or close, to those of the i.i.d. case

Computable bounds of ℓ2{\ell}^2-spectral gap for discrete Markov chains with band transition matrices

We analyse the $\ell^2(\pi)$-convergence rate of irreducible and aperiodic Markov chains with $N$-band transition probability matrix $P$ and with invariant distribution $\pi$. This analysis is heavily based on: first the study of the essential spectral radius $r\_{ess}(P\_{|\ell^2(\pi)})$ of $P\_{|\ell^2(\pi)}$ derived from Hennion's quasi-compactness criteria; second the connection between the Spectral Gap property (SG$\_2$) of $P$ on $\ell^2(\pi)$ and the $V$-geometric ergodicity of $P$. Specifically, (SG$\_2$) is shown to hold under the condition \alpha\_0 := \sum\_{{m}=-N}^N \limsup\_{i\rightarrow +\infty} \sqrt{P(i,i+{m})\, P^*(i+{m},i)}\ \textless{}\, 1 Moreover $r\_{ess}(P\_{|\ell^2(\pi)}) \leq \alpha\_0$. Effective bounds on the convergence rate can be provided from a truncation procedure.Comment: in Journal of Applied Probability, Applied Probability Trust, 2016. arXiv admin note: substantial text overlap with arXiv:1503.0220

Regular perturbation of V -geometrically ergodic Markov chains

In this paper, new conditions for the stability of V-geometrically ergodic Markov chains are introduced. The results are based on an extension of the standard perturbation theory formulated by Keller and Liverani. The continuity and higher regularity properties are investigated. As an illustration, an asymptotic expansion of the invariant probability measure for an autoregressive model with i.i.d. noises (with a non-standard probability density function) is obtained

A uniform Berry--Esseen theorem on MM-estimators for geometrically ergodic Markov chains

Let $\{X_n\}_{n\ge0}$ be a $V$-geometrically ergodic Markov chain. Given some real-valued functional $F$, define $M_n(\alpha):=n^{-1}\sum_{k=1}^nF(\alpha,X_{k-1},X_k)$, $\alpha\in\mathcal{A}\subset \mathbb {R}$. Consider an $M$ estimator $\hat{\alpha}_n$, that is, a measurable function of the observations satisfying $M_n(\hat{\alpha}_n)\leq \min_{\alpha\in\mathcal{A}}M_n(\alpha)+c_n$ with $\{c_n\}_{n\geq1}$ some sequence of real numbers going to zero. Under some standard regularity and moment assumptions, close to those of the i.i.d. case, the estimator $\hat{\alpha}_n$ satisfies a Berry--Esseen theorem uniformly with respect to the underlying probability distribution of the Markov chain.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ347 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

VITESSE DE CONVERGENCE DANS LE THÉORÈME LIMITE CENTRAL POUR CHAÎNES DE MARKOV DE PROBABILITÉ DE TRANSITION QUASI-COMPACTE

NOMBRE DE PAGES : 16International audienceLet $Q$ be a transition probability on a measurable space $E$, let $(X_n)_n$ be a Markov chain associated to $Q$, and let $\xi$ be a real-valued measurable function on $E$, and $S_n = \sum_{k=1}^{n} \xi(X_k)$. Under functional hypotheses on the action of $Q$ and its Fourier kernels $Q(t)$, we investigate the rate of convergence in the central limit theorem for the sequence $(\frac{S_n}{\sqrt n})_n$. According to the hypotheses, we prove that the rate is, either $O(n^{-\frac{\tau}{2}})$ for all $\tau<1$, or $O(n^{-\frac{1}{2}})$. We apply the spectral method of Nagaev which is improved by using a perturbation theorem of Keller and Liverani and a method of martingale difference reduction. When $E$ is not compact or $\xi$ is not bounded, the conditions required here are weaker than the ones usually imposed when the standard perturbation theorem is used. For example, in the case of $V$-geometric ergodic chains or Lipschitz iterative models, the rate of convergence in the c.l.t is $O(n^{-\frac{1}{2}})$ under a third moment condition on $\xi$