Performance of coefficient of variation estimators in ranked set sampling |
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| Authors: | Cintia Maestreli Consulin Damiane Ferreira Idemauro Antonio Rodrigues de Lara Antonino De Lorenzo Laura di Renzo |
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| Affiliation: | 1. Department of Statistics, Federal University of Paraná, Curitiba, Brazil;2. Department of Exact Sciences, Luiz de Queiroz College of Agriculture, University of S?o Paulo, Piracicaba, Brazil;3. Division of Clinical Nutrition and Nutrigenomic, Department of Biomedicine and Prevention, University of Rome ‘Tor Vergata’, Rome, Italy |
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| Abstract: | ![]()
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. |
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| Keywords: | Statistical efficiency Monte Carlo simulation parametric estimation nonparametric estimation |
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