Improving on Estimation for the Generalized Pareto Distribution
Improving on Estimation for the Generalized Pareto Distribution
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DOI：
10.1198/tech.2010.09206
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发表时间：
2010-08
期刊：
影响因子：
2.5
通讯作者：
Jin Zhang
中科院分区：
文献类型：
--
作者：
Jin Zhang
广义帕累托分布（GPD）被广泛用于模拟超过阈值的情况，例如河流的洪水水位。Zhang和Stephens（2009）提出了一种新的GPD参数估计方法，该方法基于似然方法和经验贝叶斯方法，摆脱了传统估计方法的理论和计算问题。就估计效率和偏差而言，新方法在常见情况下优于其他现有方法，但对于非常重尾分布可能表现不佳。本文对新方法进行了改进，使其自适应能力显著提高。这篇文章在网上有补充资料。
The generalized Pareto distribution (GPD) was widely used to model exceedances over thresholds, such as flood levels of rivers. Zhang and Stephens (2009) proposed a new estimation method for parameters of the GPD, which, based on the likelihood method and empirical Bayesian method, is free from the theoretical and computational problems suffered by traditional estimation approaches. In terms of estimation efficiency and bias, the new method outperforms other existing methods in common situations, but it may perform poorly for very heavy-tailed distributions. The new method is modified in this article to significantly improve its adaptivity. This article has supplementary material online.