A multivariate normal approximation for the Dirichlet density and some applications

TL;DR

This paper derives an asymptotic expansion comparing Dirichlet and multivariate normal densities, providing bounds and revisiting variance estimators, with potential applications in Gaussian process equivalence.

Contribution

It introduces an asymptotic expansion for the Dirichlet density ratio to a multivariate normal, enabling bounds and reanalysis of variance estimators.

Findings

Derived an asymptotic expansion for the Dirichlet-to-normal density ratio.

Established an upper bound on total variation distance between the measures.

Rederived the asymptotic variance of Dirichlet kernel estimators.

Abstract

In this short note, we prove an asymptotic expansion for the ratio of the Dirichlet density to the multivariate normal density with the same mean and covariance matrix. The expansion is then used to derive an upper bound on the total variation between the corresponding probability measures and rederive the asymptotic variance of the Dirichlet kernel estimators introduced by Aitchison & Lauder (1985) and studied theoretically in Ouimet (2020). Another potential application related to the asymptotic equivalence between the Gaussian variance regression problem and the Gaussian white noise problem is briefly mentioned but left open for future research.