A central limit theorem for reversible processes with non-linear growth of variance

Abstract

Kipnis and Varadhan showed that for an additive functional, $S_n$ say, of a reversible Markov chain the condition $E(S_n^2)/n\to \kappa \in (0,\infty )$ implies the convergence of the conditional distribution of $S_n/\sqrt{E(S_n^2})$, given the starting point, to the standard normal distribution. We revisit this question under the weaker condition, $E(S_n^2)=n\ell (n)$, where $\ell$ is a slowly varying function. It is shown by example that the conditional distribution of $S_n/\sqrt{E(S_n^2})$ need not converge to the standard normal distribution in this case; and sufficient conditions for convergence to a (possibly non-standard) normal distribution are developed.

11 pages