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The EM algorithm for the extended finite mixture of the factor analyzers model

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  • Zhou, Xingcai
  • Liu, Xinsheng

Abstract

This paper is devoted to extending common factors and categorical variables in the model of a finite mixture of factor analyzers based on the multivariate generalized linear model and the principle of maximum random utility in the probabilistic choice theory. The EM algorithm and Newton-Raphson algorithm are used to estimate model parameters, and then the algorithm is illustrated with a simulation study and a real example.

Suggested Citation

  • Zhou, Xingcai & Liu, Xinsheng, 2008. "The EM algorithm for the extended finite mixture of the factor analyzers model," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3939-3953, April.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:8:p:3939-3953
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    References listed on IDEAS

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    1. Fokoué, Ernest, 2005. "Mixtures of factor analyzers: an extension with covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 370-384, August.
    2. Yiu-Fai Yung, 1997. "Finite mixtures in confirmatory factor-analysis models," Psychometrika, Springer;The Psychometric Society, vol. 62(3), pages 297-330, September.
    3. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    4. Richardson, Ralph M. & Adams, Celestine C. & DeVille, Katherine c. & Penn, Jacqueline E. & Stutzman, John W. & Kraenzle, Charles A., 1994. "Farmer Cooperative Statistics, 1993," Service Reports (SR) 280693, United States Department of Agriculture, Rural Development.
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    Cited by:

    1. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.

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