Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data

IF 2 Q2 BUSINESS, FINANCE
Risks Pub Date : 2024-03-01 DOI:10.3390/risks12030045
Chudamani Poudyal
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引用次数: 0

Abstract

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to a MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods, like least squares, minimum Hellinger distance, and optimal bounded influence function, available. This paper introduces a novel robust estimation technique, the Method of Truncated Moments (MTuM), pecifically designed to estimate the tail index of a Pareto distribution from grouped data. Inferential justification of the MTuM is established by employing the central limit theorem and validating it through a comprehensive simulation study.
从分组数据中稳健估计单参数帕累托分布的尾部指数
当有完全观测到的损失严重程度样本数据集时,有许多稳健估计法可替代最大似然估计法(MLE)。然而,在处理分组损失严重程度数据时,MLE 的稳健替代方法就变得非常有限,只有最小二乘法、最小海灵格距离法和最优有界影响函数等少数几种方法可用。本文介绍了一种新颖的稳健估计技术--截断矩法(MTuM),专门用于从分组数据中估计帕累托分布的尾部指数。本文利用中心极限定理建立了 MTuM 的推论依据,并通过全面的模拟研究对其进行了验证。
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
Risks
Risks Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
3.80
自引率
22.70%
发文量
205
审稿时长
11 weeks
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