Bivariate beta regression models: joint modeling of the mean, dispersion and association parameters

In this paper a bivariate beta regression model with joint modeling of the mean and dispersion parameters is proposed, defining the bivariate beta distribution from Farlie-Gumbel-Morgenstern (FGM) copulas. This model, that can be generalized using other copulas, is a good alternative to analyze non-...

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Bibliographic Details
Published inJournal of applied statistics Vol. 41; no. 3; pp. 677 - 687
Main Authors Cepeda-Cuervo, Edilberto, Achcar, Jorge Alberto, Lopera, Liliana Garrido
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 04.03.2014
Taylor & Francis Ltd
Subjects
Applied statistics
Bayesian methods
beta distribution
beta regression models
bivariate random variables
Markov analysis
Mathematical models
MCMC methods
Monte Carlo simulation
Parameter estimation
Probability distribution
Regression analysis
Studies
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Summary:In this paper a bivariate beta regression model with joint modeling of the mean and dispersion parameters is proposed, defining the bivariate beta distribution from Farlie-Gumbel-Morgenstern (FGM) copulas. This model, that can be generalized using other copulas, is a good alternative to analyze non-independent pairs of proportions and can be fitted applying standard Markov chain Monte Carlo methods. Results of two applications of the proposed model in the analysis of structural and real data set are included.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2013.847071