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Bayesian Estimation of Morgenstern Type Bivariate Rayleigh Distribution Using Some Types of Ranked Set Sampling
Estimación bayesiana de la distribución de Rayleigh bivariada de tipo Morgenstern utilizando algunos tipos de muestreo por conjuntos clasificados
DOI:
https://doi.org/10.15446/rce.v44n2.87825Keywords:
Bayesian estimation, Concomitant ranked set sampling, Extreme ranked set sampling, Maximum ranked set sampling and Rayleigh distribution (en)Estimación bayesiana, muestreo conjunto concomitante clasificado, muestreo conjunto extremo clasificado, muestreo conjunto máximo clasificado y distribución de Rayleigh (es)
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In this paper we consider Bayesian estimation based on bivariate ranked set sample, in which units are ranked based on measurements made on an easily and exactly measurable auxiliary variable X which is correlated with the study variable Y. We obtain Bayes estimator for the scale parameter of the study variate Y, when (X, Y ) follows a Morgenstern type bivariate Rayleigh distribution. The Bayes estimators are considered based on bivari-ate ranked set sampling, extreme ranked set sampling and maximum ranked set sampling with unequal sample. The accuracy of estimation methods in this paper is illustrated using simulation study. Finally, a real data set is analyzed.
En este artículo consideramos la estimación bayesiana basada en una muestra de conjuntos clasificados bivariados, en la que las unidades se clasifican según las mediciones realizadas en una variable auxiliar X fácil y exactamente medible que se correlaciona con la variable de estudio Y. Obtuvimos el estimador de Bayes para el parámetro de escala de la variante de estudio Y, cuando (X, Y ) sigue una distribución de Rayleigh bivariada de tipo Morgenstern. Los estimadores de Bayes se consideran basados en un muestreo conjunto bivariado, un muestreo conjunto extremo clasificado y un muestreo conjunto máximo clasificado con muestra desigual. La precisión de los métodos de estimación en este documento se ilustra mediante el estudio de simulación. Finalmente, se analiza un conjunto de datos real.
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