An EM algorithm for estimation in the Mixture Transition Distribution model

TL;DR

This paper introduces an EM algorithm for efficient parameter estimation in the Mixture Transition Distribution (MTD) model, improving usability and performance over previous methods, especially for high-order models applied to DNA sequences.

Contribution

It develops a novel EM algorithm for MTD parameter estimation, simplifying the process and enhancing performance compared to existing algorithms.

Findings

EM algorithm outperforms previous methods in ease of use

High-order MTD models better fit DNA sequences than fully parametrized Markov chains

Software implementation available in seq++ library

Abstract

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