A factor mixture analysis model for multivariate binary data [article]

Silvia Cagnone, Cinzia Viroli
2010 arXiv   pre-print
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
more » ... 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.
arXiv:1010.2314v1 fatcat:rwhyb7tyffdqlpls7fcba5tlai