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Multivariate extreme value distributions for stationary Gaussian sequences

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  • Amram, Fred

Abstract

Under weak regularity conditions of the covariance sequence, it is shown that the joint limiting distribution of the maxima on each coordinate of a stationary Gaussian multivariate sequence is that of independent random variables with marginal Gumbel distributions.

Suggested Citation

  • Amram, Fred, 1985. "Multivariate extreme value distributions for stationary Gaussian sequences," Journal of Multivariate Analysis, Elsevier, vol. 16(2), pages 237-240, April.
    • Handle: RePEc:eee:jmvana:v:16:y:1985:i:2:p:237-240
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    Citations

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    Cited by:

    1. Kabluchko, Zakhar, 2009. "Extremes of space-time Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 119(11), pages 3962-3980, November.
    2. J. Kuhn & M. Mandjes & T. Taimre, 2019. "Practical Aspects of False Alarm Control for Change Point Detection: Beyond Average Run Length," Methodology and Computing in Applied Probability, Springer, vol. 21(1), pages 25-42, March.
    3. James, Barry & James, Kang & Qi, Yongcheng, 2007. "Limit distribution of the sum and maximum from multivariate Gaussian sequences," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 517-532, March.
    4. Bucher, Axel & Segers, Johan, 2013. "Extreme value copula estimation based on block maxima of a multivariate stationary time series," LIDAM Discussion Papers ISBA 2013049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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