Latent class analysis of incomplete data via an entropy-based criterion

Chantal Larose, Ofer Harel, Katarzyna Kordas, Dipak K. Dey

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)


Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.
Original languageEnglish
Pages (from-to)107-121
Number of pages15
JournalStatistical Methodology
Early online date10 May 2016
Publication statusPublished - Sep 2016


  • Entropy
  • Latent class analysis
  • Missing data
  • Model selection
  • Multiple imputation


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