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A Law of the Iterated Logarithm for the Sample Covariance Matrix

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	A Law of the Iterated Logarithm for the Sample Covariance Matrix
2.	Creator	Author's name, affiliation, country	Steven J. Sepanski; Saginaw Valley State University
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)	law of the iterated logarithm, sample covariance, central limit theorem, generalized domain of attraction, multivariate t statistic, extreme values, operator normalization, self normalization
3.	Subject	Subject classification	Primary 60F15; secondary 60F05
4.	Description	Abstract	For a sequence of independent identically distributed Euclidean random vectors, we prove a law of the iterated logarithm for the sample covariance matrix when o(log log n) terms are omitted. The result is proved under the hypothesis that the random vectors belong to the generalized domain of attraction of the multivariate Gaussian law. As an application, we obtain a bounded law of the iterated logarithm for the multivariate t-statistic.
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)
7.	Date	(YYYY-MM-DD)	2003-05-20
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/1070
10.	Identifier	Digital Object Identifier	10.1214/ECP.v8-1070
11.	Source	Journal/conference title; vol., no. (year)	Electronic Communications in Probability; Vol 8
12.	Language	English=en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
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