  The cluster-weighted model (CWM) is a mixture model with random covariates
which allows for flexible clustering and density estimation of a random vector
composed by a response variable and by a set of covariates. In this class of
models, the generalized linear exponential CWM is here introduced especially
for modeling bivariate data of mixed-type. Its natural counterpart, in the
family of latent class models, is also defined. Maximum likelihood parameter
estimates are derived using the EM algorithm and model selection is carried out
using the Bayesian information criterion (BIC). Artificial and real data are
finally considered to exemplify and appreciate the proposed model.
