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Identifying groups of variables with the potential of being large simultaneously

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  • Chiapino, Mael
  • Sabourin, Anne
  • Segers, Johan

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  • Chiapino, Mael & Sabourin, Anne & Segers, Johan, 2018. "Identifying groups of variables with the potential of being large simultaneously," LIDAM Discussion Papers ISBA 2018006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2018006
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    References listed on IDEAS

    as
    1. Einmahl, J.H.J. & Krajina, A. & Segers, J., 2011. "An M-Estimator for Tail Dependence in Arbitrary Dimensions," Other publications TiSEM 27508aa0-9825-4d9e-b1f4-1, Tilburg University, School of Economics and Management.
    2. Martin Schlather, 2003. "A dependence measure for multivariate and spatial extreme values: Properties and inference," Biometrika, Biometrika Trust, vol. 90(1), pages 139-156, March.
    3. Peng, L., 1999. "Estimation of the coefficient of tail dependence in bivariate extremes," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 399-409, July.
    4. Emma F. Eastoe & Jonathan A. Tawn, 2012. "Modelling the distribution of the cluster maxima of exceedances of subasymptotic thresholds," Biometrika, Biometrika Trust, vol. 99(1), pages 43-55.
    5. Einmahl, John H. J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Stochastic Processes and their Applications, Elsevier, vol. 70(1), pages 31-58, October.
    6. Goix, Nicolas & Sabourin, Anne & Clémençon, Stephan, 2017. "Sparse representation of multivariate extremes with applications to anomaly detection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 12-31.
    7. Holger Drees, 1998. "On Smooth Statistical Tail Functionals," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 187-210, March.
    8. Alexandra Ramos & Anthony Ledford, 2009. "A new class of models for bivariate joint tails," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 219-241, January.
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    Cited by:

    1. Maël Chiapino & Stephan Clémençon & Vincent Feuillard & Anne Sabourin, 2020. "A multivariate extreme value theory approach to anomaly clustering and visualization," Computational Statistics, Springer, vol. 35(2), pages 607-628, June.

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