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.
All Science Journal Classification (ASJC) codes
- Mathematics (miscellaneous)
- Psychology (miscellaneous)
- Statistics, Probability and Uncertainty
- Library and Information Sciences