IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

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

Listed author(s):
  • 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)

Registered author(s):

    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.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    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

    in new window

    Handle: RePEc:pab:rmcpee:v:7:y:2009:i:1:p:3-30
    Contact details of provider: Postal:
    Carretera de Utrera km.1, 41013 Sevilla

    Phone: + 34 954 34 8913
    Fax: + 34 954 34 9339
    Web page:

    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:pab:rmcpee:v:7:y:2009:i:1:p:3-30. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Publicación Digital - UPO)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.