A New Difference-Cum-Exponential Type Estimator of Finite Population Mean in Simple Random Sampling

• JAVID SHABBIR ^[1] ; ABDUL HAQ ^[1] ; SAT GUPTA ^[2]
  1. [1] Quaid-i-Azam University

     Quaid-i-Azam University

     Pakistán

     
  2. [2] University of North Carolina at Greensboro

     University of North Carolina at Greensboro

     Township of Morehead, Estados Unidos

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 37, Nº. 1, 2014, págs. 199-211
• Idioma: inglés
• DOI: 10.15446/rce.v37n1.44366
• Títulos paralelos:
  • Un nuevo estimador tipo diferencia-cum-exponencial de la media de una población finita en muestras aleatorias simple
• Enlaces
  • Texto completo
• Dialnet Métricas: 2 Citas
• Resumen
  • español

    Información auxiliar se utiliza con frecuencia para mejorar la precisión de los estimadores al estimar los parámetros poblacionales desconocidos. En este trabajo, se propone un nuevo tipo de diferencia-cum-exponencial estimador de la población finita implicar el uso de información auxiliar en muestreo aleatorio simple. Las expresiones para el sesgo y el error cuadrático medio del estimador propuesto se obtienen en primer orden de aproximación. Se muestra teóricamente, que el estimador propuesto es siempre más eficiente que la media de la muestra, la relación de, producto, regresión y varios otros estimadores existentes considerados aquí. Un estudio empírico utilizando 10 conjuntos de datos también se lleva a cabo para validar los resultados teóricos.

  • English

    Auxiliary information is frequently used to improve the accuracy of the estimators when estimating the unknown population parameters. In this paper, we propose a new difference-cum-exponential type estimator for the finite population mean using auxiliary information in simple random sampling. The expressions for the bias and mean squared error of the proposed estimator are obtained under first order of approximation. It is shown theoretically, that the proposed estimator is always more efficient than the sample mean, ratio, product, regression and several other existing estimators considered here. An empirical study using 10 data sets is also conducted to validate the theoretical findings.

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