A Weak Law of Large Numbers for the Sample Covariance Matrix

Steven J. Sepanski (Saginaw Valley State University)
Zhidong Pan (Saginaw Valley State University)

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.

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Pages: 73-76

Publication Date: March 20, 2000

DOI: 10.1214/ECP.v5-1020

References

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