2015
DOI: 10.1016/j.spl.2015.04.007
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Maxima of a triangular array of multivariate Gaussian sequence

Abstract: It is known that the normalized maxima of a sequence of independent and identically distributed bivariate normal random vectors with correlation coefficient ρ ∈ (−1, 1) is asymptotically independent, which may seriously underestimate extreme probabilities in practice. By letting ρ depend on the sample size and go to one with certain rate, Hüsler and Reiss (1989) showed that the normalized maxima can become asymptotically dependent. In this paper, we extend such a study to a triangular array of multivariate Gau… Show more

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Cited by 5 publications
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“…Using analogues of conditions of [15], which allow for strong local dependence among variables while keeping their asymptotic independence, [13] generalized the results of [15] and [14] to multivariate stationary Gaussian triangular arrays. The limiting distribution of the normalized maxima for Gaussian random vectors was derived, and [14] established the limit law for the bivariate stationary Gaussian triangular arrays.…”
Section: Introductionmentioning
confidence: 99%
“…Using analogues of conditions of [15], which allow for strong local dependence among variables while keeping their asymptotic independence, [13] generalized the results of [15] and [14] to multivariate stationary Gaussian triangular arrays. The limiting distribution of the normalized maxima for Gaussian random vectors was derived, and [14] established the limit law for the bivariate stationary Gaussian triangular arrays.…”
Section: Introductionmentioning
confidence: 99%