Semi-parametric approach to the Hasofer–Wang and Greenwood statistics in extremes
Semi-parametric approach to the Hasofer–Wang and Greenwood statistics in extremes
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极端情况下 Hasofer-Wang 和 Greenwood 统计的半参数方法
DOI：
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发表时间：
2007
期刊：
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通讯作者：
M. Isabel Fraga Alves
中科院分区：
文献类型：
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作者：
C. Neves;M. Isabel Fraga Alves
摘要
本文讨论了极端吸引域的统计选择问题的半参数方法。依赖于规则变分理论的概念，它调查的渐近性质的Hasstern和王的测试统计量的基础上采取的k个上限从一个样本大小为n，当k的行为作为一个中间序列kn，而不是保持固定，而样本大小的增加。在此过程中，格林伍德型检验统计量的建议，这是有用的区分重尾分布。这两个测试程序的有限样本的行为进行了评估，根据模拟研究。然后将测试程序应用于三个真实的数据集。
Abstract
This paper deals with the semi-parametric approach to the problem of statistical choice of extreme domains of attraction. Relying on concepts of regular variation theory, it investigates the asymptotic properties of Hasofer and Wang’s test statistic based on the k upper extremes taken from a sample of size n, when k behaves as an intermediate sequence kn rather than remaining fixed while the sample size increases. In the process a Greenwood type test statistic is proposed which turns out to be useful in discriminating heavy-tailed distributions. The finite sample behavior of both testing procedures is evaluated in the light of a simulation study. The testing procedures are then applied to three real data sets.