The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf
.
A factor mixture analysis model for multivariate binary data
[article]
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
arXiv:1010.2314v1
fatcat:rwhyb7tyffdqlpls7fcba5tlai