A formula for the tail probability of a multivariate normal distribution and its applications

DOI10.1006/jmva.2001.2031zbMath1006.62047OpenAlexW2078647503MaRDI QIDQ700147

Kesar Singh, Regina Y. Liu, Juerg Hüsler

Publication date: 30 September 2002

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.2001.2031

zbMATH Keywords

tail probabilitygrowth ratemaximum deviationGumbel distributionmultivariate normal distribution

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)

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Classification in general finite dimensional spaces with the \(k\)-nearest neighbor rule ⋮ On extremal behavior of Gaussian chaos ⋮ The almost sure limit theorem for the maxima and minima of strongly dependent Gaussian vector sequences ⋮ Kesar Singh's contributions to statistical methodology ⋮ Deep neural network expression of posterior expectations in Bayesian PDE inversion ⋮ Asymptotic expansion of Gaussian chaos via probabilistic approach ⋮ Asymptotics of the norm of elliptical random vectors ⋮ COMPARISON OF INFERENTIAL METHODS IN PARTIALLY IDENTIFIED MODELS IN TERMS OF ERROR IN COVERAGE PROBABILITY ⋮ Extremal behavior of squared Bessel processes attracted by the Brown-Resnick process

Cites Work

• Extremes and related properties of random sequences and processes
• Multivariate analysis by data depth: Descriptive statistics, graphics and inference. (With discussions and rejoinder)
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