Performance of coefficient of variation estimators in ranked set sampling

被引：4

作者：

Consulin, Cintia Maestreli ^{[1
]}

Ferreira, Damiane ^{[1
]}

Rodrigues de Lara, Idemauro Antonio ^{[2
]}

De Lorenzo, Antonino ^{[3
]}

di Renzo, Laura ^{[3
]}

Taconeli, Cesar Augusto ^{[1
]}

机构：

[1] Univ Fed Parana, Dept Stat, Curitiba, Parana, Brazil

[2] Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Exact Sci, Piracicaba, Brazil

[3] Univ Roma Tor Vergata, Dept Biomed & Prevent, Div Clin Nutr & Nutrigenom, Rome, Italy

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2018年 / 88卷 / 02期

关键词：

Statistical efficiency; Monte Carlo simulation; parametric estimation; nonparametric estimation; 62Dxx; 62F10; 62Fxx; 62Gxx; VARIANCE;

D O I：

10.1080/00949655.2017.1381959

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we propose and evaluate the performance of different parametric and nonparametric estimators for the population coefficient of variation considering Ranked Set Sampling (RSS) under normal distribution. The performance of the proposed estimators was assessed based on the bias and relative efficiency provided by a Monte Carlo simulation study. An application in anthropometric measurements data from a human population is also presented. The results showed that the proposed estimators via RSS present an expressively lower mean squared error when compared to the usual estimator, obtained via Simple Random Sampling. Also, it was verified the superiority of the maximum likelihood estimator, given the necessary assumptions of normality and perfect ranking are met.

引用

页码：221 / 234

页数：14

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