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

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

2010-05-22

arxiv.org/abs/1005.4124v1

Mathematics

Probability

11 pages

Scientific paper

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.

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