A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
Giordan, Marco (Istituto Agrario San Michele all'Adige (IASMA) Research and Innovation Centre)
Wehrens, Ron (Istituto Agrario San Michele all'Adige (IASMA) Research and Innovation Centre)

Date:	2015
Abstract:	Likelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated if good starting values are not provided. In this paper we discuss several approaches that can partially avoid these problems providing a good trade-off between efficiency and stability. The performances of these approaches are compared on high-dimensional real and simulated data.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Levenberg-marquardt algorithm ; Re-parametrization ; Starting values ; Metabolomics data
Published in:	SORT : statistics and operations research transactions, Vol. 39 Núm. 1 (January-June 2015) , p. 109-126 (Articles) , ISSN 2013-8830

Adreça alternativa: https://raco.cat/index.php/SORT/article/view/294380

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