Large deviations for unbounded additive functionals of a Markov process with discrete time (noncompact case)

Additive Functionals for Discrete-time Markov Chains with Applications to Birth–death Processes

In this paper we are interested in bounding or calculating the additive functionals of the first return time on a set for discrete-time Markov chains on a countable state space, which is motivated by investigating ergodic theory and central limit theorems. To do so, we introduce the theory of the minimal nonnegative solution. This theory combined with some other techniques is proved useful for ...

Process Level Moderate Deviations for Stabilizing Functionals

Functionals of spatial point process often satisfy a weak spatial dependence condition known as stabilization. In this paper we prove process level moderate deviation principles (MDP) for such functionals, which is a level-3 result for empirical point fields as well as a level2 result for empirical point measures. The level-3 rate function coincides with the so-called specific information. We s...

8 Limit Theorems for Additive Functionals of a Markov Chain

Consider a Markov chain {X n } n≥0 with an ergodic probability measure π. Let Ψ a function on the state space of the chain, with α-tails with respect to π, α ∈ (0, 2). We find sufficient conditions on the probability transition to prove convergence in law of N 1/α N n Ψ(X n) to a α-stable law. A " martingale approximation " approach and " coupling " approach give two different sets of condition...

Kac’s moment formula and the Feynman–Kac formula for additive functionals of a Markov process

Mark Kac introduced a method for calculating the distribution of the integral Av= ∫ T 0 v(Xt) dt for a function v of a Markov process (Xt; t¿0) and a suitable random time T , which yields the Feynman–Kac formula for the moment-generating function of Av. We review Kac’s method, with emphasis on an aspect often overlooked. This is Kac’s formula for moments of Av, which may be stated as follows. F...

Multiplicative Operator Functionals of a Markov Process