Censoring heavy-tail count distributions for parameter estimation with an application to stable distributions

被引：0

作者：

Di Noia, Antonio ^{[1
,2
]}

Marcheselli, Marzia ^{[3
]}

Pisani, Caterina ^{[3
,5
]}

Pratelli, Luca ^{[4
]}

机构：

[1] Swiss Fed Inst Technol, Zurich, Switzerland

[2] Univ Svizzera italiana, Lugano, Switzerland

[3] Univ Siena, Dept Econ & Stat, Siena, Italy

[4] Naval Acad, Livorno, Italy

[5] Univ Siena, Dept Econ & Stat, Pzza S Francesco 8, I-53100 Siena, Italy

来源：

STATISTICS & PROBABILITY LETTERS | 2023年 / 202卷

关键词：

Asymptotic normality; Consistency; Data-driven; Probability generating function;

D O I：

10.1016/j.spl.2023.109903

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A new approach based on censoring and moment criterion is introduced for parameter estimation of count distributions when the probability generating function is available even though a closed form of the probability mass function and/or finite moments do not exist.& COPY; 2023 Elsevier B.V. All rights reserved.

引用

页数：5

共 11 条

[1] Parameter estimation of stable distributions
Fan, ZZ
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