二项—Gumbel复合极值分布的参数估计
发布时间：2018-08-20 16:05
【摘要】：在金融风险评估、事故预测、保险索赔等领域的研究中,极值理论已发展成为一种重要的统计学方法。Gumbel分布是一种常用的极值分布函数,并逐渐成为了对于随机变量极端变异性建模的重要工具。文章将二项分布与Gumbel分布函数复合,提出了一种新的复合极值分布函数即二项-Gumbel分布。重点介绍了极值理论以及二项分布与Gumbel分布复合函数,运用极大似然估计(MLE)对二项-Gumbel复合分布的各种参数进行估计,并通过计算机模拟得KS检验统计量的临界值。
[Abstract]:In the field of financial risk assessment, accident prediction, insurance claims and so on, extreme value theory has developed into an important statistical method. Gumbel distribution is a commonly used extreme value distribution function. It has gradually become an important tool for modeling extreme variability of random variables. In this paper, binomial distribution is combined with Gumbel distribution function, and a new compound extreme distribution function, binomial Gumbel distribution, is proposed. The extreme value theory and the compound function of binomial distribution and Gumbel distribution are introduced emphatically. The parameters of binomial Gumbel compound distribution are estimated by maximum likelihood estimation (MLE), and the critical values of KS test statistics are obtained by computer simulation.
【作者单位】： 兰州财经大学统计学院;中国人民大学应用统计科学研究中心;中国人民大学统计学院;
【分类号】：O212.1
，
本文编号：2194202
[Abstract]:In the field of financial risk assessment, accident prediction, insurance claims and so on, extreme value theory has developed into an important statistical method. Gumbel distribution is a commonly used extreme value distribution function. It has gradually become an important tool for modeling extreme variability of random variables. In this paper, binomial distribution is combined with Gumbel distribution function, and a new compound extreme distribution function, binomial Gumbel distribution, is proposed. The extreme value theory and the compound function of binomial distribution and Gumbel distribution are introduced emphatically. The parameters of binomial Gumbel compound distribution are estimated by maximum likelihood estimation (MLE), and the critical values of KS test statistics are obtained by computer simulation.
【作者单位】： 兰州财经大学统计学院;中国人民大学应用统计科学研究中心;中国人民大学统计学院;
【分类号】：O212.1
，
本文编号：2194202
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