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Abstract Details
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Activity Number:
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583
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #301156 |
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Title:
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On Semiparametric Modeling of Heavy-Tailed Distributions
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Author(s):
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Dieter Schell*+ and Jan Beran
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Companies:
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University of Konstanz and University of Konstanz
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Address:
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, Konstanz, 78464, DE
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Keywords:
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heavy tails ;
tail index
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Abstract:
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A combination of a Pareto density and suitable basis functions is used to define a large class of heavy-tailed distributions. The corresponding coefficients are estimated simultaneously with the tail index. Consistency and asymptotic normality are derived. Moreover, estimation based on an increasing number of basis functions is considered. The resulting class of tail index estimators is compared to optimal nonparametric methods known in the literature. Simulations and data examples illustrate the theoretical results.
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