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On Pickands coordinates in arbitrary dimensions

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  • Falk, Michael
  • Reiss, Rolf-Dieter

Abstract

Pickands coordinates were introduced as a crucial tool for the investigation of bivariate extreme value models. We extend their definition to arbitrary dimensions and, thus, we can generalize many known results for bivariate extreme value and generalized Pareto models to higher dimensions and arbitrary extreme value margins. In particular we characterize multivariate generalized Pareto distributions (GPs) and spectral [delta]-neighborhoods of GPs in terms of best attainable rates of convergence of extremes, which are well-known results in the univariate case. A sufficient univariate condition for a multivariate distribution function (df) to belong to the domain of attraction of an extreme value df is derived. Bounds for the variational distance in peaks-over-threshold models are established, which are based on Pickands coordinates.

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  • Falk, Michael & Reiss, Rolf-Dieter, 2005. "On Pickands coordinates in arbitrary dimensions," Journal of Multivariate Analysis, Elsevier, vol. 92(2), pages 426-453, February.
    • Handle: RePEc:eee:jmvana:v:92:y:2005:i:2:p:426-453
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    References listed on IDEAS

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    1. Falk, Michael & Reiss, Rolf-Dieter, 2003. "Efficient estimators and LAN in canonical bivariate POT models," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 190-207, January.
    2. Deheuvels, Paul, 1991. "On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions," Statistics & Probability Letters, Elsevier, vol. 12(5), pages 429-439, November.
    3. Einmahl, John H.J. & de Haan, Laurens & Sinha, Ashoke Kumar, 1997. "Estimating the spectral measure of an extreme value distribution," Stochastic Processes and their Applications, Elsevier, vol. 70(2), pages 143-171, October.
    4. Falk, Michael & Reiss, Rolf-Dieter, 2001. "Estimation of canonical dependence parameters in a class of bivariate peaks-over-threshold models," Statistics & Probability Letters, Elsevier, vol. 52(3), pages 233-242, April.
    5. Jiménez, Javier Rojo & Villa-Diharce, Enrique & Flores, Miguel, 2001. "Nonparametric Estimation of the Dependence Function in Bivariate Extreme Value Distributions," Journal of Multivariate Analysis, Elsevier, vol. 76(2), pages 159-191, February.
    6. Hüsler, Jürg & Reiss, Rolf-Dieter, 1989. "Maxima of normal random vectors: Between independence and complete dependence," Statistics & Probability Letters, Elsevier, vol. 7(4), pages 283-286, February.
    7. Omey, E. & Rachev, S. T., 1991. "Rates of convergence in multivariate extreme value theory," Journal of Multivariate Analysis, Elsevier, vol. 38(1), pages 36-50, July.
    8. de Haan, L. & Peng, L., 1997. "Rates of Convergence for Bivariate Extremes," Journal of Multivariate Analysis, Elsevier, vol. 61(2), pages 195-230, May.
    9. de Oliveira, J. Tiago, 1989. "Intrinsic estimation of the dependence structure for bivariate extremes," Statistics & Probability Letters, Elsevier, vol. 8(3), pages 213-218, August.
    10. Falk, Michael & Reiss, Rolf Dieter, 2002. "A characterization of the rate of convergence in bivariate extreme value models," Statistics & Probability Letters, Elsevier, vol. 59(4), pages 341-351, October.
    11. Deheuvels, Paul & Tiago de Oliveira, José, 1989. "On the non-parametric estimation of the bivariate extreme-value distributions," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 315-323, September.
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    Cited by:

    1. Kojadinovic, Ivan & Yan, Jun, 2010. "Nonparametric rank-based tests of bivariate extreme-value dependence," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2234-2249, October.
    2. Barme-Delcroix, Marie-Francoise & Gather, Ursula, 2007. "Limit laws for multidimensional extremes," Statistics & Probability Letters, Elsevier, vol. 77(18), pages 1750-1755, December.
    3. Zhang, Dabao & Wells, Martin T. & Peng, Liang, 2008. "Nonparametric estimation of the dependence function for a multivariate extreme value distribution," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 577-588, April.
    4. Charpentier, A. & Fougères, A.-L. & Genest, C. & Nešlehová, J.G., 2014. "Multivariate Archimax copulas," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 118-136.
    5. Falk, Michael, 2005. "On the generation of a multivariate extreme value distribution with prescribed tail dependence parameter matrix," Statistics & Probability Letters, Elsevier, vol. 75(4), pages 307-314, December.
    6. Falk, Michael & Guillou, Armelle, 2008. "Peaks-over-threshold stability of multivariate generalized Pareto distributions," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 715-734, April.
    7. Claudia Klüppelberg & Gabriel Kuhn & Liang Peng, 2008. "Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 701-718, December.
    8. Frick, Melanie & Reiss, Rolf-Dieter, 2009. "Expansions of multivariate Pickands densities and testing the tail dependence," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1168-1181, July.

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