  We consider the segmentation problem of univariate distributions from the
exponential family with multiple parameters. In segmentation, the choice of the
number of segments remains a difficult issue due to the discrete nature of the
change-points. In this general exponential family distribution framework, we
propose a penalized log-likelihood estimator where the penalty is inspired by
papers of L. Birg\'e and P. Massart. The resulting estimator is proved to
satisfy an oracle inequality. We then further study the particular case of
categorical variables by comparing the values of the key constants when derived
from the specification of our general approach and when obtained by working
directly with the characteristics of this distribution. Finally, a simulation
study is conducted to assess the performance of our criterion for the
exponential distribution, and an application on real data modelled by the
categorical distribution is provided.
