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Consistency of information criteria for model selection with missing data

Author

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  • Abdelaziz El Matouat
  • Freedath Djibril Moussa
  • Hassania Hamzaoui

Abstract

In this paper, we investigate the consistency of the Expectation Maximization (EM) algorithm-based information criteria for model selection with missing data. The criteria correspond to a penalization of the conditional expectation of the complete data log-likelihood given the observed data and with respect to the missing data conditional density. We present asymptotic properties related to maximum likelihood estimation in the presence of incomplete data and we provide sufficient conditions for the consistency of model selection by minimizing the information criteria. Their finite sample performance is illustrated through simulation and real data studies.

Suggested Citation

  • Abdelaziz El Matouat & Freedath Djibril Moussa & Hassania Hamzaoui, 2016. "Consistency of information criteria for model selection with missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(23), pages 6900-6914, December.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:23:p:6900-6914
    DOI: 10.1080/03610926.2014.972568
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