Scandinavian Working Papers in Economics
HomeAboutSeriesSubject/JEL codesAdvanced Search
School of Business, Örebro University Working Papers, School of Business, Örebro University

No 2017:5:
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 () and Nestor Parolya ()

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 to in nity such that p=n ! c 2 [0;+1) when the sample covariance matrix does not need to be invertible and p=n ! c 2 [0; 1) otherwise.

Keywords: Normal mixtures; skew normal distribution; large dimensional asymptotics; stochastic representation; random matrix theory; (follow links to similar papers)

JEL-Codes: C00; C13; C15; (follow links to similar papers)

30 pages, August 22, 2017

Before downloading any of the electronic versions below you should read our statement on copyright.
Download GhostScript for viewing Postscript files and the Acrobat Reader for viewing and printing pdf files.

Downloadable files:

wp-5-2017.pdf    PDF-file
Download Statistics

Questions (including download problems) about the papers in this series should be directed to Forskningsadministratör ()
Report other problems with accessing this service to Sune Karlsson () or Helena Lundin ().

Programing by
Design by Joakim Ekebom

Handle: RePEc:hhs:oruesi:2017_005 This page was generated on 2017-08-22 21:40:29