New estimators of the extreme value index under random right censoring, for heavy-tailed distributions
New estimators of the extreme value index under random right censoring, for heavy-tailed distributions
复制标题
随机右审查下重尾分布的极值指数的新估计量
DOI：
10.1007/s10687-014-0189-6
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发表时间：
2014
期刊：
影响因子：
1.3
通讯作者：
R. Worms
中科院分区：
文献类型：
--
作者：
J. Worms;R. Worms
本文基于Kaplan-Meier积分的思想和Leurgans（1987）的合成数据方法，提出了随机截尾样本框架下极值指数估计的新方法。这些思想在重尾情况下得到了发展，并对Hill估计量进行了修正，证明了Hill估计量在一阶条件下的一致性。仿真结果表明，与现有的Hill估计器相比，这两种方法都具有良好的性能
This paper presents new approaches for the estimation of the extreme value index in the framework of randomly censored samples, based on the ideas of Kaplan-Meier integration and the synthetic data approach of Leurgans (1987). These ideas are developed here in the heavy-tailed case, and lead to modifications of the Hill estimator, for which the consistency is proved under first order conditions. Simulations exhibit good performances of the two approaches, compared to the only existing adaptation of the Hill estimator in this context