¹University of Isfahan, Department of Statistics, Isfahan, Iran. PhD. Email: e.zamanzade@sci.ui.ac.ir; ehsanzamanzadeh@yahoo.com
²Hakim Sabzevari University, Department of Statistics, Sabzevar, Iran. PhD. Email: mahdizadeh.m@live.com

This article deals with entropy estimation using ranked set sampling (RSS). Some estimators are developed based on the empirical distribution function and its nonparametric maximum likelihood competitor. The suggested entropy estimators have smaller root mean squared errors than the other entropy estimators in the literature. The proposed estimators are then used to construct goodness of fit tests for inverse Gaussian distribution.

Key words: Judgment ranking, Goodness of fit test, Entropy estimation.

Este artículo trata sobre la estimación de entropía usando muestras de rango ordenado (RSS). Algunos estimadores se desarrollan con base en distribuciones empíricas y si estimación no paramétrica de máxima verosimilitud. Los estimadores de entropía sugeridos tienen menor raíz del error de cuadrados medios que otros reportados en literatura. Los estimadores propuestos son usados para construir pruebas de bondad de ajuste para distribuciones inversas Gaussianas.

Palabras clave: Bondad de ajuste, Estimación de entropía, Ranking de juicios.

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