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On coupling constructions and rates in the CLT for dependent summands with applications to the antivoter model and weighted \(U\)-statistics. (English) Zbl 0890.60019
Summary: This paper deals with rates of convergence in the CLT for certain types of dependency. The main idea is to combine a modification of a theorem of Stein, requiring a coupling construction, with a dynamic set-up provided by a Markov structure that suggests natural coupling variables. More specifically, given a stationary Markov chain \({\mathbf X}^{(t)}\), and a function \(U= U({\mathbf X}^{(t)})\), we propose a way to study the proximity of \(U\) to a normal random variable when the state space is large. We apply the general method to the study of two problems. In the first, we consider the antivoter chain \({\mathbf X}^{(t)}= \{X^{(t)}_i\}_{i\in{\mathcal V}}\), \(t= 0,1,\dots\), where \({\mathcal V}\) is the vertex set of an \(n\)-vertex regular graph, and \(X^{(t)}_i= +1\) or \(-1\). The chain evolves from time \(t\) to \(t+1\) by choosing a random vertex \(i\), and a random neighbor of it \(j\), and setting \(X^{(t+ 1)}_i= -X^{(t)}_j\) and \(X^{(t+ 1)}_k= X^{(t)}_k\) for all \(k\neq i\). For a stationary antivoter chain, we study the normal approximation of \(U_n= U^{(t)}_n= \sum_i X^{(t)}_i\) for large \(n\) and consider some conditions on sequences of graphs such that \(U_n\) is asymptotically normal, a problem posed by Aldous and Fill. The same approach may also be applied in situations where a Markov chain does not appear in the original statement of a problem but is constructed as an auxiliary device. This is illustrated by considering weighted \(U\)-statistics. In particular, we are able to unify and generalize some results on normal convergence for degenerate weighted \(U\)-statistics and provide rates.

MSC:
60F05 Central limit and other weak theorems
60K35 Interacting random processes; statistical mechanics type models; percolation theory
62E20 Asymptotic distribution theory in statistics
60J10 Markov chains (discrete-time Markov processes on discrete state spaces)
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