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Computing & Statistics

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Statistics of Extremes and Applications


Extreme value analysis deals with the statistical modeling and analysis of extremal observations in a sample, in univariate, multivariate as well as in infinite dimensional space. The restriction of the statistical analysis to the extremal observations is justified by the fact that this part of the data can be of outstanding importance. Floods, hurricanes, extreme claim sizes, etc. obviously exhibit a large risk scenario.

Extremal observations may be defined in different ways, either as maxima or as exceedances above high thresholds. The first approach led to the "annual maxima method", ruled by extreme value distributions. It is well-known that exceedances over high thresholds can reasonably be modeled only by a generalized Pareto distribution. However only in recent years has this alternative approach to the traditional annual maxima method been widely spread outside the academic world as well. Extreme value analysis has its pecularities and cannot be looked in isolation, but instead it must be linked to other branches of statistics as well.


Co-Chairs:

Michael Falk, University of Wuerzburg, Germany. E-mail: Send
Armelle Guillou, University of Strasbourg, France. E-mail: Send
Ivette Gomes, University of Lisbon, Portugal. E-mail: Send
Johan Segers, University of Louvain-la-Neuve, Belgium. E-mail: Send

Members

    1. Miguel de Carvalho, University of Lausanne, Switzerland.
    2. Anthony Davison, University of Lausanne, Switzerland.
    3. Michael Falk, University of Wuerzburg, Germany.
    4. Isabel Fraga Alves, University of Lisbon, Portugal.
    5. John Einmahl, Tilburg University, Netherlands.
    6. Anne-Laure Fougeres, University of Lyon, France.
    7. Jorge Milhazes Freitas, University of Porto, Portugal.
    8. Carlo Gaetan, University of Venice, Italy.
    9. Ivette Gomes, University of Lisbon, Portugal.
    10. Yuri Goegebeur, University of Southern Denmark, Denmark.
    11. Armelle Guillou, University of Strasbourg, France.
    12. Juerg Huesler, University of Bern, Switzerland.
    13. Ivan Kojadinovic, University of Pau, France.
    14. Claudia Klueppelberg, Technical University of Munich, Germany.
    15. Thomas Mikosch, University of Copenhagen, Denmark.
    16. Ana Cristina Moreira de Freitas, University of Porto, Portugal.
    17. Alexandra Ramos, University of Porto, Portugal.
    18. Mathieu Ribatet, University of Montpellier, France.
    19. Christian Y. Robert, University of Lyon, France.
    20. Paulo Araujo Santos, University of Lisbon, Portugal.
    21. Martin Schlather, University of Goettingen, Germany.
    22. Johan Segers, University of Louvan-la_Neuve, Belgium.
    23. Catalin Starica, University of Neuchatel, Switzerland.
    24. Jenny Wadsworth, University of Lancaster, UK.
    25. Chen Zhou, Economics and Research Division, De Nederlandsche Bank, Netherlands.

Created by Computing & Statistics Working Group 2012