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.

A factor mixture analysis model for multivariate binary data / Cagnone S.; Viroli C.. - In: STATISTICAL MODELLING. - ISSN 1471-082X. - STAMPA. - 12:(2012), pp. 257-277. [10.1177/1471082X1101200303]

A factor mixture analysis model for multivariate binary data

CAGNONE, SILVIA;VIROLI, CINZIA
2012

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.
2012
A factor mixture analysis model for multivariate binary data / Cagnone S.; Viroli C.. - In: STATISTICAL MODELLING. - ISSN 1471-082X. - STAMPA. - 12:(2012), pp. 257-277. [10.1177/1471082X1101200303]
Cagnone S.; Viroli C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/103045
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