  The Mixture Transition Distribution (MTD) model was introduced by Raftery to
face the need for parsimony in the modeling of high-order Markov chains in
discrete time. The particularity of this model comes from the fact that the
effect of each lag upon the present is considered separately and additively, so
that the number of parameters required is drastically reduced. However, the
efficiency for the MTD parameter estimations proposed up to date still remains
problematic on account of the large number of constraints on the parameters. In
this paper, an iterative procedure, commonly known as Expectation-Maximization
(EM) algorithm, is developed cooperating with the principle of Maximum
Likelihood Estimation (MLE) to estimate the MTD parameters. Some applications
of modeling MTD show the proposed EM algorithm is easier to be used than the
algorithm developed by Berchtold. Moreover, the EM Estimations of parameters
for high-order MTD models led on DNA sequences outperform the corresponding
fully parametrized Markov chain in terms of Bayesian Information Criterion. A
software implementation of our algorithm is available in the library seq++ at
http://stat.genopole.cnrs.fr/seqpp
