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A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data

By Marco Giordan and Ron Wehrens

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.Peer Reviewe

Topics: Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica, Levenberg-Marquardt algorithm, re-parametrization, starting values, metabolomics data, Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations, Classificació AMS::62 Statistics::62F Parametric inference
Publisher: Institut d'Estadística de Catalunya
Year: 2015
OAI identifier: oai:upcommons.upc.edu:2117/88522
Journal:

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