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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)

Abstract

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.

Keywords

Multiple Imputation Gaussian Mixture Model Data Augmentation Label Switching Trace Plot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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