Central limit theorems for functionals of large sample covariance matrix and mean vector in matrix-variate location mixture of normal distributions

Taras Bodnar, Stepan Mazur, Nestor Parolya

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3 Citations (Scopus)
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Abstract

In this paper, we consider the asymptotic distributions of functionals of the sample covariance matrix and the sample mean vector obtained under the assumption that the matrix of observations has a matrix-variate location mixture of normal distributions. The central limit theorem is derived for the product of the sample covariance matrix and the sample mean vector. Moreover, we consider the product of the inverse sample covariance matrix and the mean vector for which the central limit theorem is established as well. All results are obtained under the large-dimensional asymptotic regime, where the dimension p and the sample size n approach infinity such that p/n→c ∈ [0, + ∞) when the sample covariance matrix does not need to be invertible and p/n→c ∈ [0,1) otherwise.

Original languageEnglish
Pages (from-to)636-660
Number of pages25
JournalScandinavian Journal of Statistics
Volume46
Issue number2
DOIs
Publication statusPublished - 1 Jun 2019

Keywords

  • large-dimensional asymptotics
  • normal mixtures
  • random matrix theory
  • skew normal distribution
  • stochastic representation

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