Electronic Journal of Probability

Large Deviations Asymptotics and the Spectral Theory of Multiplicatively Regular Markov Processes

Ioannis Kontoyiannis and Sean Meyn

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Abstract

In this paper we continue the investigation of the spectral theory and exponential asymptotics of primarily discrete-time Markov processes, following Kontoyiannis and Meyn (2003). We introduce a new family of nonlinear Lyapunov drift criteria, which characterize distinct subclasses of geometrically ergodic Markov processes in terms of simple inequalities for the nonlinear generator. We concentrate primarily on the class of multiplicatively regular Markov processes, which are characterized via simple conditions similar to (but weaker than) those of Donsker-Varadhan. For any such process $\{\Phi(t)\}$ with transition kernel $P$ on a general state space $X$, the following are obtained. Spectral Theory: For a large class of (possibly unbounded) functionals $F$ on $X$, the kernel $\hat P(x,dy) = e^{F(x)} P(x,dy)$ has a discrete spectrum in an appropriately defined Banach space. It follows that there exists a "maximal," well-behaved solution to the "multiplicative Poisson equation," defined as an eigenvalue problem for $\hat P$. Multiplicative Mean Ergodic Theorem: Consider the partial sums of this process with respect to any one of the functionals $F$ considered above. The normalized mean of their moment generating function (and not the logarithm of the mean) converges to the above maximal eigenfunction exponentially fast. Multiplicative regularity: The Lyapunov drift criterion under which our results are derived is equivalent to the existence of regeneration times with finite exponential moments for the above partial sums. Large Deviations: The sequence of empirical measures of the process satisfies a large deviations principle in a topology finer that the usual tau-topology, generated by the above class of functionals. The rate function of this LDP is the convex dual of logarithm of the above maximal eigenvalue, and it is shown to coincide with the Donsker-Varadhan rate function in terms of relative entropy. Exact Large Deviations Asymptotics: The above partial sums are shown to satisfy an exact large deviations expansion, analogous to that obtained by Bahadur and Ranga Rao for independent random variables.

Article information

Source
Electron. J. Probab., Volume 10 (2005), paper no. 3, 61-123.

Dates
Accepted: 24 February 2005
First available in Project Euclid: 1 June 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejp/1464816804

Digital Object Identifier
doi:10.1214/EJP.v10-231

Mathematical Reviews number (MathSciNet)
MR2120240

Zentralblatt MATH identifier
1079.60067

Keywords
Markov process large deviations entropy Lyapunov function empirical measures nonlinear generator large deviations principle

Rights
This work is licensed under aCreative Commons Attribution 3.0 License.

Citation

Kontoyiannis, Ioannis; Meyn, Sean. Large Deviations Asymptotics and the Spectral Theory of Multiplicatively Regular Markov Processes. Electron. J. Probab. 10 (2005), paper no. 3, 61--123. doi:10.1214/EJP.v10-231. https://projecteuclid.org/euclid.ejp/1464816804


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