A latent class probit model for analyzing pick any/N data

Geert De Soete, Wayne S. DeSarbo

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


A latent class probit model is developed in which it is assumed that the binary data of a particular subject follow a finite mixture of multivariate Bermoulli distributions. An EM algorithm for fitting the model is described and a Monte Carlo procedure for testing the number of latent classes that is required for adequately describing the data is discussed. In the final section, an application of the latent class probit model to some intended purchase data for residential telecommunication devices is reported.

Original languageEnglish (US)
Pages (from-to)45-63
Number of pages19
JournalJournal of Classification
Issue number1
StatePublished - Jan 1991

All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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