A multivariate normal approximation for the Dirichlet density and some applications. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Ouimet, Frédéric
• Is Part Of: Stat. Volume 11:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-01
• Subjects: asymptotic statistics -- asymptotic variance -- density estimation -- Dirichlet distribution -- expansion -- Gaussian approximation -- multivariate normal -- nonparametric statistics -- normal approximation -- smoothing -- total variation; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.410
• Languages: English
• ISSNs: 2049-1573

Abstract

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 and 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.