A Latent Class Probit Model for Analyzing Pick Any/N Data

Abstract

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.

Keywords: Probit Model, Latent Class Analysis, Finite Mixture Distribution, EM Algorithm, Monte Carlo Significance Test, Market Segmentation