A maximum likelihood approximation method for Dirichlet's parameter estimation
Fiche publication
Date publication
janvier 2008
Auteurs
Membres identifiés du Cancéropôle Est :
Dr POCH Olivier
Tous les auteurs :
Wicker N, Muller J, Kalathur RKR, Poch O
Lien Pubmed
Résumé
Dirichlet distributions are natural choices to analyse data described by frequencies or proportions since they are the simplest known distributions for such data apart from the uniform distribution. They are often used whenever proportions are involved, for example, in text-mining, image analysis, biology or as a prior of a multinomial distribution in Bayesian statistics. As the Dirichlet distribution belongs to the exponential family, its parameters can be easily inferred by maximum likelihood. Parameter estimation is usually performed with the Newton-Raphson algorithm after an initialisation step using either the moments or Ronning's methods. However this initialisation can result in parameters that lie outside the admissible region. A simple and very efficient alternative based on a maximum likelihood approximation is presented. The advantages of the presented method compared to two other methods are demonstrated on synthetic data sets as well as for a practical biological problem: the clustering of protein sequences based on their amino acid compositions. (c) 2007 Elsevier B.V All rights reserved.
Référence
Comput Stat Data Anal. 2008 Jan 1;52(3):1315-22