A new class of random vector entropy estimators and its applications in testing statistical hypotheses

被引：111

作者：

Goria, MN

Leonenko, NN

Mergel, VV

Inverardi, PLN ^{[1
]}

机构：

[1] Univ Trent, Dept Comp & Management Sci, I-38100 Trento, Italy

[2] Cardiff Univ, Sch Math, Cardiff CF24 4YH, S Glam, Wales

[3] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2005年 / 17卷 / 03期

基金：

澳大利亚研究理事会; 英国工程与自然科学研究理事会;

关键词：

entropy; multivariate density; estimator; goodness-of-fit test; testing independence; Monte Carlo methods;

D O I：

10.1080/104852504200026815

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：

This paper proposes a new class of estimators of an unknown entropy of random vector. Its asymptotic unbiasedness and consistency are proved. Further, this class of estimators is used to build both goodness-of-fit and independence tests based on sample entropy. A simulation study indicates that the test involving the proposed entropy estimate has higher power than other well-known competitors under heavy tailed alternatives which are frequently used in many financial applications.

引用

页码：277 / 297

页数：21

共 37 条

[1] NONPARAMETRIC ESTIMATION OF ENTROPY FOR ABSOLUTELY CONTINUOUS DISTRIBUTIONS [J].