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A Weak Law of Large Numbers for the Sample Covariance Matrix

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	A Weak Law of Large Numbers for the Sample Covariance Matrix
2.	Creator	Author's name, affiliation, country	Steven J. Sepanski; Saginaw Valley State University
2.	Creator	Author's name, affiliation, country	Zhidong Pan; Saginaw Valley State University
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)	Law of large numbers, affine normalization, samplecovariance, central limit theorem, domain of attraction, generalized domain of attraction, multivariate t statistic
4.	Description	Abstract	In this article we consider the sample covariance matrix formed from a sequence of independent and identically distributed random vectors from the generalized domain of attraction of the multivariate normal law. We show that this sample covariance matrix, appropriately normalized by a nonrandom sequence of linear operators, converges in probability to the identity matrix.
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)
7.	Date	(YYYY-MM-DD)	2000-03-20
8.	Type	Status & genre	Peer-reviewed Article
8.	Type	Type
9.	Format	File format	PDF
10.	Identifier	Uniform Resource Identifier	http://ecp.ejpecp.org/article/view/1020
10.	Identifier	Digital Object Identifier	10.1214/ECP.v5-1020
11.	Source	Journal/conference title; vol., no. (year)	Electronic Communications in Probability; Vol 5
12.	Language	English=en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
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