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Mixtures of factor analyzers: an extension with covariates

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  • Fokoué, Ernest

Abstract

This paper examines the analysis of an extended finite mixture of factor analyzers (MFA) where both the continuous latent variable (common factor) and the categorical latent variable (component label) are assumed to be influenced by the effects of fixed observed covariates. A polytomous logistic regression model is used to link the categorical latent variable to its corresponding covariate, while a traditional linear model with normal noise is used to model the effect of the covariate on the continuous latent variable. The proposed model turns out be in various ways an extension of many existing related models, and as such offers the potential to address some of the issues not fully handled by those previous models. A detailed derivation of an EM algorithm is proposed for parameter estimation, and latent variable estimates are obtained as by-products of the overall estimation procedure.

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  • Fokoué, Ernest, 2005. "Mixtures of factor analyzers: an extension with covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 370-384, August.
    • Handle: RePEc:eee:jmvana:v:95:y:2005:i:2:p:370-384
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    2. James C. Slaughter & Amy H. Herring & John M. Thorp, 2009. "A Bayesian Latent Variable Mixture Model for Longitudinal Fetal Growth," Biometrics, The International Biometric Society, vol. 65(4), pages 1233-1242, December.

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