ADAPTIVE ESTIMATORS OF THE GENERAL PARETO DISTRIBUTION PARAMETERS UNDER RANDOM CENSORSHIP AND APPLICATION

IF 0.3 Q4 MULTIDISCIPLINARY SCIENCES
Kouider Mohammed Ridha, Idiou Nesrine, Benatia Fatah
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引用次数: 0

Abstract

In this article, we introduce adaptive estimators for parameters of the (GPD) Generalized Pareto Distribution under censored data via the KIB-estimator. The KIB-estimator is based on the Maximum Likelihood Estimates (MLE) by the exceedances over the threshold t under random censoring which was developed by [1]. Hence, it was proved that the KIB-estimator is Maximum Likelihood (ML) estimator with the uncensored case. We use the standardized MLE based on the exceedances on the uncensored situation which converge to a centered bivariate normal distribution. Whose found by [2] to standardized our adaptive KIB estimator of the GPD parameters under random censorship. As an application, we establish the asymptotic normality of an estimator of the excess-of- loss reinsurance premium for heavy-tailed distribution, through the adapted KIB estimator of GPD under censored data.
随机约束下一般pareto分布参数的自适应估计及其应用
在这篇文章中,我们通过KIB估计器介绍了截尾数据下(GPD)广义Pareto分布参数的自适应估计。KIB估计器是基于[1]提出的在随机截尾条件下超过阈值t的最大似然估计(MLE)。因此,证明了KIB估计量是非审查情况下的最大似然估计量。我们使用标准化MLE,该MLE基于在未经审查的情况下的超越,其收敛到中心二变量正态分布。[2]的发现标准化了我们在随机审查下的GPD参数的自适应KIB估计器。作为一个应用,我们通过截尾数据下GPD的自适应KIB估计,建立了重尾分布超额损失再保险保费估计量的渐近正态性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Science and Arts
Journal of Science and Arts MULTIDISCIPLINARY SCIENCES-
自引率
25.00%
发文量
57
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