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

Geert De Soete (UGent) and W Desarbo
(1991) JOURNAL OF CLASSIFICATION. 8(1). p.45-63
Author
Organization
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 Bernoulli 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
REPRESENTATION, SEGMENTATION, THRESHOLD-MODEL, MAXIMUM-LIKELIHOOD, MARKET SEGMENTATION, MONTE-CARLO SIGNIFICANCE TEST, EM ALGORITHM, FINITE MIXTURE DISTRIBUTION, LATENT CLASS ANALYSIS, PROBIT MODEL

Citation

Please use this url to cite or link to this publication:

MLA
De Soete, Geert, and W Desarbo. “A Latent Class Probit Model for Analyzing Pick any/N Data.” JOURNAL OF CLASSIFICATION 8.1 (1991): 45–63. Print.
APA
De Soete, G., & Desarbo, W. (1991). A latent class probit model for analyzing pick any/N data. JOURNAL OF CLASSIFICATION, 8(1), 45–63.
Chicago author-date
De Soete, Geert, and W Desarbo. 1991. “A Latent Class Probit Model for Analyzing Pick any/N Data.” Journal of Classification 8 (1): 45–63.
Chicago author-date (all authors)
De Soete, Geert, and W Desarbo. 1991. “A Latent Class Probit Model for Analyzing Pick any/N Data.” Journal of Classification 8 (1): 45–63.
Vancouver
1.
De Soete G, Desarbo W. A latent class probit model for analyzing pick any/N data. JOURNAL OF CLASSIFICATION. 1991;8(1):45–63.
IEEE
[1]
G. De Soete and W. Desarbo, “A latent class probit model for analyzing pick any/N data,” JOURNAL OF CLASSIFICATION, vol. 8, no. 1, pp. 45–63, 1991.
@article{227272,
  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 Bernoulli 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.},
  author       = {De Soete, Geert and Desarbo, W},
  issn         = {0176-4268},
  journal      = {JOURNAL OF CLASSIFICATION},
  keywords     = {REPRESENTATION,SEGMENTATION,THRESHOLD-MODEL,MAXIMUM-LIKELIHOOD,MARKET SEGMENTATION,MONTE-CARLO SIGNIFICANCE TEST,EM ALGORITHM,FINITE MIXTURE DISTRIBUTION,LATENT CLASS ANALYSIS,PROBIT MODEL},
  language     = {eng},
  number       = {1},
  pages        = {45--63},
  title        = {A latent class probit model for analyzing pick any/N data},
  volume       = {8},
  year         = {1991},
}