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
Additional contact information
Taras Bodnar: Stockholm University, Postal: Department of Mathematics, Stockholm University, SE-10691 Stockholm, Sweden, http://www.su.se/profiles/tbodn-1.219689
Stepan Mazur: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/personal/stepan_mazur
No 2017:5, Working Papers from Örebro University, School of Business
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 (search for similar items in EconPapers)
JEL-codes: C00 C13 C15 (search for similar items in EconPapers)
Pages: 30 pages
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
https://www.oru.se/globalassets/oru-sv/institution ... rs2017/wp-5-2017.pdf (application/pdf)
Journal Article: Central limit theorems for functionals of large sample covariance matrix and mean vector in matrix‐variate location mixture of normal distributions (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:hhs:oruesi:2017_005
Access Statistics for this paper
More papers in Working Papers from Örebro University, School of Business Örebro University School of Business, SE - 701 82 ÖREBRO, Sweden. Contact information at EDIRC.
Bibliographic data for series maintained by ().