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

4 Scopus citations

Abstract

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 (US)
Pages (from-to)107-121
Number of pages15
JournalStatistical Methodology
Volume32
DOIs
StatePublished - Sep 1 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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