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A Weak Law of Large Numbers for the Sample Covariance Matrix

  
@article{ECP1020,
	author = {Steven Sepanski and Zhidong Pan},
	title = {A Weak Law of Large Numbers for the Sample Covariance Matrix},
	journal = {Electron. Commun. Probab.},
	fjournal = {Electronic Communications in Probability},
	volume = {5},
	year = {2000},
	keywords = {Law of large numbers, affine normalization, samplecovariance, central limit theorem, domain of attraction, generalized domain of attraction, multivariate t statistic},
	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.},
	pages = {no. 8, 73-76},
	issn = {1083-589X},
	doi = {10.1214/ECP.v5-1020},    
        url = {http://ecp.ejpecp.org/article/view/1020}}