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arXiv · 1010.2314

A factor mixture analysis model for multivariate binary data

Silvia Cagnone·Cinzia Viroli
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Abstract

The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional assumption of Gaussian distributed factors by a finite mixture of multivariate Gaussians. The aim of the proposed model is twofold: it allows to achieve dimension reduction when the data are dichotomous and, simultaneously, it performs model based clustering in the latent space. Model estimation is obtained by means of a maximum likelihood method via a generalized version of the EM algorithm. In order to evaluate the performance of the model a simulation study and two real applications are illustrated.

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Silvia Cagnone, Cinzia Viroli. 2010-10-12. A factor mixture analysis model for multivariate binary data. https://arxiv.org/abs/1010.2314

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