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

Geert De Soete; Wayne S. DeSarbo

doi:10.1007/BF02616247

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

Geert De Soete, Wayne S. DeSarbo

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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.

Original language	English (US)
Pages (from-to)	45-63
Number of pages	19
Journal	Journal of Classification
Volume	8
Issue number	1
DOIs	https://doi.org/10.1007/BF02616247
State	Published - Jan 1991

All Science Journal Classification (ASJC) codes

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

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10.1007/BF02616247

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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.",

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

Abstract

All Science Journal Classification (ASJC) codes

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