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  • PABLO MARSHALL

    ()
    (Escuela de Administración, Pontificia Universidad Católica de Chile)

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    Abstract

    Statistical procedures for missing data have improved significantly in the last years. This study put the missing data in context and makes a revision of the recent literature for the case that the missing data problem is ignorable. In an application, based in real data of a psychographic profile study, several different methods to treat missing data are implemented. The results of this exercise show that most of the traditional imputation procedures induce bias and do not consider the whole variability in the estimates. Multiple imputation, based on Bayesian models and data augmentation gives the best results in the application.

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    File URL: http://eacc10.puc.cl/files/ABT/Contenidos/Vol-5-N1/Marshall%20Pablo.pdf
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    Bibliographic Info

    Article provided by Escuela de Administracion. Pontificia Universidad Católica de Chile. in its journal ABANTE.

    Volume (Year): 5 (2002)
    Issue (Month): 1 ()
    Pages: 53-76

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    Handle: RePEc:pch:abante:v:5:y:2002:i:1:p:53-76

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    Related research

    Keywords: Missing data; Multiple imputation; Data augmentation; Bayesian models;

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
    2. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
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