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Métodos de imputación para el tratamiento de datos faltantes: aplicación mediante R/Splus = Imputation methods to handle the problem of missing data: an application using R/Splus

  • Muñoz Rosas, Juan Francisco


    (Departamento de Métodos Cuantitativos para la Economía y la Empresa. Universidad de Granada)

  • Alvarez Verdejo, Encarnación


    (Departamento de Métodos Cuantitativos para la Economía y la Empresa. Universidad de Granada)

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    La aparición de datos faltantes es un problema común en la mayoría de las encuestas llevadas a cabo en distintos ámbitos. Una técnica tradicional y muy conocida para el tratamiento de datos faltantes es la imputación. La mayoría de los estudios relacionados con los métodos de imputación se centran en el problema de la estimación de la media y su varianza y están basados en diseños muestrales simples tales como el muestreo aleatorio simple. En este trabajo se describen los métodos de imputación más conocidos y se plantean bajo el contexto de un diseño muestral general y para el caso de diferentes mecanismos de respuesta. Mediante estudios de simulación Monte Carlo basados en datos reales extraídos del ámbito de la economía y la empresa, analizamos las propiedades de varios métodos de imputación en la estimación de otros parámetros que también son utilizados con frecuencia en la práctica, como son las funciones de distribución y los cuantiles. Con el fin de que los métodos de imputación descritos en este trabajo se puedan implementar y usar con mayor facilidad, se proporcionan sus códigos en los lenguajes de programación R y Splus. = Missing values are a common problem in many sampling surveys, and imputation is usually employed to compensate for non-response. Most imputation methods are based upon the problem of the mean estimation and its variance, and they also assume simple sampling designs such as the simple random sampling without replacement. In this paper we describe some imputation methods and define them under a general sampling design. Different response mechanisms are also discussed. Assuming some populations based upon real data extracted from the context of the economy and business, Monte Carlo simulations are carried out to analyze the properties of the various imputation methods in the estimation of parameters such as distribution functions and quantiles. The various imputation methods are implemented using the popular statistical softwares R and Splus, and codes are here presented.

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    Article provided by Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration in its journal Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration.

    Volume (Year): 7 (2009)
    Issue (Month): 1 (June)
    Pages: 3-30

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    Handle: RePEc:pab:rmcpee:v:7:y:2009:i:1:p:3-30
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    1. Yves G. Berger & Chris J. Skinner, 2003. "Variance estimation for a low income proportion," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 457-468.
    2. Yves G. Berger & J. N. K. Rao, 2006. "Adjusted jackknife for imputation under unequal probability sampling without replacement," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 531-547.
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