On Multiple Imputation Through Finite Gaussian Mixture Models

  • Marco Di Zio
  • Ugo Guarnera
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Multiple Imputation is a frequently used method for dealing with partial nonresponse. In this paper the use of finite Gaussian mixture models for multiple imputation in a Bayesian setting is discussed. Simulation studies are illustrated in order to show performances of the proposed method.


Multiple Imputation Gaussian Mixture Model Data Augmentation Label Switching Trace Plot 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marco Di Zio
    • 1
  • Ugo Guarnera
    • 1
  1. 1.Istituto Nazionale di StatisticaRomaItaly

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