Largest entries of sample correlation matrices from equi-correlated normal populations. (English) Zbl 1435.62077

The authors proved two important theorems about the limiting distributions of the largest entry outside the diagonal of the sample covariance / correlation matrices generated by a random sample from a high-dimensional normal distribution, assuming that: the normal distribution has the equi-correlation structure (with parameter \(\rho\)), that \(p \to \infty\) and \(\log(p) = o(n^{1/3})\), where \(p\) is the random vector dimension and \(n\) is the number of observations. The theory developed is exemplified by means of an application to a statistical test involving a high-dimensional normal distribution (testing if the covariance matrix is diagonal). Finally, in the “discussions” subsection the authors conclude that behaviors of limiting distributions depend on the value of \(\rho\). The limits are the normal distribution if \(\rho\) is reasonably large; the limiting distributions are the extreme-value distribution if \(\rho\) is tiny. Moreover, the authors also figure out the regime to differentiate the two scenarios.


62E20 Asymptotic distribution theory in statistics
62H10 Multivariate distribution of statistics
62H15 Hypothesis testing in multivariate analysis
60F05 Central limit and other weak theorems
62G32 Statistics of extreme values; tail inference
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