Irregular sets and Central Limit Theorems for dependent triangular arrays

TL;DR

This paper extends the analysis of asymptotic Gaussian behavior of sums over irregular sets to weakly dependent triangular arrays, with applications to regression models involving dependent and independent random fields.

Contribution

It generalizes previous results to triangular arrays and demonstrates the applicability to regression models with dependent and independent components.

Findings

CLT holds for weakly dependent triangular arrays.

Asymptotically measurable sets ensure Gaussian limits.

Application to regression models with dependent fields.

Abstract

In previous papers, we studied the asymptotic behaviour of $S_N(A,X)=(2N+1)^{-d/2}\sum_{n \in A_N} X_n,$ where $X$ is a centered, stationary and weakly dependent random field, and $A_N=A \cap [-N,N]^d$, $A \subset \mathbb{Z}^d$. This leads to the definition of asymptotically measurable sets, which enjoy the property that $S_N(A;X)$ has a Gaussian weak limit for any $X$ belonging to a certain class. Here we extend this type of results to the case of weakly dependent triangular arrays and present an application of this technique to regression models. Indeed, we prove that CLT and related results hold for $X_n^N=\varphi(\xi_n^N,Y_n^N), n \in \mathbb{Z}^d$, where $\varphi$ satisfies certain regularity conditions, $\xi$ and $Y$ are independent random fields, $\xi$ is weakly dependent and $Y$ satisfies some Strong Law of Large Numbers.