Statistical Modelling 10 (2010), 441–460
Heteroscedastic factor mixture analysis
Angela Montanari
and
Cinzia Viroli
Department of Statistics,
University of Bologna,
via Belle Arti, 41
I–40126 Bologna
Italy
eMail: cinzia.viroli@unibo.it
Abstract:
When data come from an unobserved heterogeneous population, common factor
analysis is not appropriate to estimate the underlying constructs of interest.
By replacing the traditional assumption of Gaussian distributed factors by a
finite mixture of multivariate Gaussians, the unobserved heterogeneity can be
modelled by latent classes. In so doing, we obtain a particular factor mixture
analysis with heteroscedastic components. In this paper, the model is presented
and a maximum likelihood estimation procedure via the expectation–maximization
algorithm is developed. We also show that the approach well performs as a
dimensionally reduced model-based clustering. Two real applications are
illustrated and performances are compared to standard model-based clustering
methods.
Keywords:
EM algorithm; factor analysis; Gaussian mixture models; model-based clustering
Downloads:
Example data and
R-code:
File fma_1.0.zip
is intended for MS-Windows and file
fma_1.0.tar.gz
for Linux.
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