A high-dimensional test for the equality of the smallest eigenvalues of a covariance matrix

James R. Schott

Journal of Multivariate Analysis, 2006, vol. 97, issue 4, 827-843

Abstract: For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic which is robust against high dimensionality. In this paper, we consider a natural generalization of their statistic for the test that the smallest eigenvalues of a covariance matrix are equal. Some inequalities are obtained for sums of eigenvalues and sums of squared eigenvalues. These bounds permit us to obtain the asymptotic null distribution of our statistic, as the dimensionality and sample size go to infinity together, by using distributional results obtained by Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102]. Some empirical results comparing our test with the likelihood ratio test are also given.

Keywords: Principal; components; analysis; Sums; of; eigenvalues (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (16)

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