¹Hakim Sabzevari University, Department of Statistics, Sabzevar, Iran. Assistant Professor. Email: mahdizadeh.m@live.com, noniid@yahoo.com

Statistical methods based on ranked set sampling (RSS) often lead to marked improvement over analogous methods based on simple random sampling (SRS). Entropy has been influential in the development of measures of fit of parametric models to the data. This article develops goodness-of-fit tests of the Laplace distribution based on sample entropy when data are collected according to some RSS-based schemes. For each design, critical values of the corresponding test statistic are estimated, by means of simulation, for some sample sizes. A Monte Carlo study on the power of the new tests is performed for several alternative distributions and sample sizes in order to compare our proposal with available method in SRS. Simulation results show that RSS and its variations lead to tests giving higher power than the test based on SRS.

Key words: Entropy estimation, Goodness-of-fit test, Ranked set sampling.

Los métodos estadísticos basados en muestreo de rango ordenado a menudo son una considerable mejora que el muestreo aleatorio simple. La medida de entropía ha sido influencial en el desarrollo de medidas de ajuste de modelos paramétricos. Este artículo propone pruebas de bondad de ajuste de la distribución Laplace basada en la entropía muestral cuando se usan estructuras basadas en muestras de rango ordenado. Para cada diseño, los valores críticos del correspondiente estadístico de prueba son estimados por medio de simulaciones para diferentes tamaños de muestra. Un estudio de Monte Carlo de la potencia de los nuevos tests es implementado para diferentes distribuciones alternas y tamaños de muestra con el fin de comparar el método propuesto con otros disponibles. La simulación muestra que el muestreo de rango ordenado y sus variaciones brindan mayor potencia que los métodos basados en muestreo aleatorio simple.

Palabras clave: entropía, muestreo rango ordenado, prueba de bondad de ajuste.

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