On higher-order moment and cumulant estimation

被引：6

作者：

Chan, Lok Hang ^{[1
]}

Chen, Kun ^{[2
,3
]}

Li, Chun Xue ^{[1
]}

Wong, Chung Wang ^{[1
]}

Yau, Chun Yip ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China

[2] Southwestern Univ Finance & Econ, Sch Stat, Chengdu, Sichuan, Peoples R China

[3] Southwestern Univ Finance & Econ, Inst Chinese Financial Studies, Chengdu, Sichuan, Peoples R China

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2020年 / 90卷 / 04期

关键词：

Conditional expectation; central moment; sufficient statistic; uniformly minimum-variance unbiased estimator; PREFERENCE;

D O I：

10.1080/00949655.2019.1700987

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Moments and cumulants are involved in statistical analysis for a wide range of fields. A natural and popular approach to moment and cumulant estimation is based on the sample average. However, it is well known that these sample estimates usually perform poorly. In this paper, we derive uniformly minimum-variance unbiased estimator for raw moment, centred moment, and cumulant of any order for a number of common distributions. Extensive simulation studies demonstrate that the proposed estimators can perform much better than the corresponding sample average estimators.

引用

页码：747 / 771

页数：25

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