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Activity Number:
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320
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300459 |
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Title:
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Robust Likelihood Methods Based on the Skew-T and Related Distributions
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Author(s):
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Adelchi Azzalini and Marc G. Genton*+
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Companies:
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University of Padova and University of Geneva
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Address:
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Bd du Pont-d'Arve 40, Geneva 4, International, CH-1211 , Switzerland
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Keywords:
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Kurtosis ; Multivariate ; Robustness ; Skewness
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Abstract:
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The robustness problem is tackled by adopting a parametric class of distributions flexible enough to match the behavior of the observed data. In a variety of practical cases, one reasonable option is to consider distributions which include parameters to regulate their skewness and kurtosis. As a specific representative of this approach, the skew-t distribution is explored in more detail, and reasons are given to adopt this option as a sensible general-purpose compromise between robustness and simplicity, both of treatment and of interpretation of the outcome. Some theoretical arguments, outcomes of a few simulation experiments and various wide-ranging examples with real data are provided in support of the claim.
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