Convergence of moments in a Markov-chain central limit theorem

AbstractLet (Xi)i=0∞ be a V-uniformly ergodic Markov chain on a general state space, and let π be its stationary distribution. For g : χ → R, define It is shown that if |g| ≤ V1/n for a positive integer n, then ExWk(g)n converges to the n-th moment of a normal random variable with expectation 0 and variance This extends the existing Markov-chain central limit theorems, according to which expectations of bounded functionals of Wk(g) converge.We also derive nonasymptotic bounds for the error in approximating the moments of Wk (g) by the normal moments. These yield easy bounds of all feasible polynomial orders, and exponential bounds as well under some circumstances, for the probabilities of large deviations by the empirical measure along the Markov chain path Xi