Convergence in distribution norms in the CLT for non identical distributed random variables

TL;DR

This paper investigates convergence in distribution norms in the CLT for non-identically distributed variables, extending classical results to derivatives of test functions and providing applications like invariance principles and small ball estimates.

Contribution

It introduces bounds for derivatives of test functions in the CLT convergence, relaxing regularity conditions and broadening applicability beyond smooth functions.

Findings

Established bounds for derivatives of test functions in CLT convergence

Extended CLT results to measurable and bounded functions under new conditions

Applied results to occupation times, small ball probabilities, and roots of random polynomials

Abstract

We study the convergence in distribution norms in the Central Limit Theorem for non identical distributed random variables that is $$ \varepsilon_{n}(f):={\mathbb{E}}\Big(f\Big(\frac 1{\sqrt n}\sum_{i=1}^{n}Z_{i}\Big)\Big)-{\mathbb{E}}\big(f(G)\big)\rightarrow 0 $$ where $Z_{i}$ are centred independent random variables and $G$ is a Gaussian random variable. We also consider local developments (Edgeworth expansion). This kind of results is well understood in the case of smooth test functions $f$. If one deals with measurable and bounded test functions (convergence in total variation distance), a well known theorem due to Prohorov shows that some regularity condition for the law of the random variables $Z_{i}$, $i\in {\mathbb{N}}$, on hand is needed. Essentially, one needs that the law of $ Z_{i}$ is locally lower bounded by the Lebesgue measure (Doeblin's condition). This topic is also widely discussed in the literature. Our main contribution is to discuss convergence in distribution norms, that is to replace the test function $f$ by some derivative $\partial_{\alpha }f$ and to obtain upper bounds for $\varepsilon_{n}(\partial_{\alpha }f)$ in terms of the infinite norm of $f$. Some applications are also discussed: an invariance principle for the occupation time for random walks, small balls estimates and expected value of the number of roots of trigonometric polynomials with random coefficients.