Nonparametric estimation of the entropy using a ranked set sample

被引：4

作者：

Amini, Morteza ^{[1
,2
]}

Mahdizadeh, Mahdi ^{[3
]}

机构：

[1] Univ Tehran, Coll Sci, Sch Math Stat & Comp Sci, Dept Stat, POB 14155-6455, Tehran, Iran

[2] Inst Res Fundamental Sci IPM, Sch Biol Sci, Tehran, Iran

[3] Hakim Sabzevari Univ, Dept Stat, Sabzevar, Iran

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2017年 / 46卷 / 09期

关键词：

Kernel density estimation; Multi-stage ranked set sampling; Nonlinear dependency; Optimal bandwidth selection; POPULATION; DENSITY;

D O I：

10.1080/03610918.2016.1208229

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 article is concerned with nonparametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estimator in simple random sampling. The applications of the proposed estimator to the mutual information estimation as well as estimation of the Kullback-Leibler divergence are provided. Several Monte-Carlo simulation studies are conducted to examine the performance of the estimator. The results are applied to the longleaf pine (Pinus palustris) trees and the body fat percentage datasets to illustrate applicability of theoretical results.

引用

页码：6719 / 6737

页数：19

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