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Abstract Details
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Activity Number:
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514
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #304124 |
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Title:
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A Goodness-of-Fit Test for Identifying the Maximum Domain of Attraction
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Author(s):
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Tatsuyoshi Okimoto*+ and Hiroaki Kaido
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Companies:
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Hitotsubashi University and Boston University
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Address:
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2-1-2 Hitoshbshi, Tokyo 101-8439, , Japan
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Keywords:
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Extreme value theory ;
Maximum domain of attraction ;
Khmaladze transform
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Abstract:
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This paper studies an asymptotically pivotal goodness-of-fit test statistic for identifying the maximum domain of attraction. The asymptotic pivotal test statistic is constructed by applying Khmaladze's martingale transform to an empirical process based on the observations that exceed a random threshold. Because of the statistic's omnibus nature, it has nontrivial power against both Weibull and Frechet domains. Through Monte Carlo experiments, we compare the performance of our test with three other existing statistics. Our simulation results produce the following results. i) None of the three statistics dominates ours in terms of global power. ii) Our statistic achieves the highest power against the Frechet domain, which slightly dominates the power of Neves and Fraga-Alves (2007) test statistic. iii) Our statistic has the second highest power following Hasofer and Wang (1992) test statistic against the Weibull domain.
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