A note on nonparametric estimation of bivariate tail dependence
A note on nonparametric estimation of bivariate tail dependence
复制标题
关于二元尾部依赖性非参数估计的注记
DOI：
10.1515/strm-2013-1143
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Axel Bücher
中科院分区：
文献类型：
--
作者：
Axel Bücher
摘要 如果累积分布函数已知，则尾部依赖性的非参数估计可以基于边际的标准化。在本文中，如果忽略边际的附加知识并且估计量基于等级，则渐近效率更高。对于流行的 Clayton 和 Gumbel-Hougaard 模型，这两个估计量之间的差异非常大。简短的模拟研究表明渐进结论可以转移到有限样本。
Abstract Nonparametric estimation of tail dependence can be based on a standardization of the marginals if their cumulative distribution functions are known. In this paper it is shown to be asymptotically more efficient if the additional knowledge of the marginals is ignored and estimators are based on ranks. The discrepancy between the two estimators is shown to be substantial for the popular Clayton and Gumbel–Hougaard models. A brief simulation study indicates that the asymptotic conclusions transfer to finite samples.