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Modelling count data using the logratio-normal-multinomial distribution - RP:4899

Author, as appears in the article.:Palarea-Albaladejo, Javier
Mateu-Figueras, Glòria
Martín-Fernández, Josep Antoni
Comas-Cufí, Marc
Journal publication year:2020
Publication Type:##rt.metadata.pkp.peerReviewed##
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
Abstract:The logratio-normal-multinomial distribution is a count data model resulting from compounding a multinomial distribution for the counts with a multivariate logratio-normal distribution for the multinomial event probabilities. However, the logratio-normal-multinomial probability mass function does not admit a closed form expression and, consequently, numerical approximation is required for parameter estimation. In this work, different estimation approaches are introduced and evaluated. We concluded that estimation based on a quasi-Monte Carlo Expectation-Maximisation algorithm provides the best overall results. Building on this, the performances of the Dirichlet-multinomial and logratio-normal-multinomial models are compared through a number of examples using simulated and real count data.
Keywords:count data
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