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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis

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Volume 21, Issue 1


Bayesian beta regression models with joint mean and dispersion modeling

Edilberto Cepeda-Cuervo / Liliana Garrido
Published Online: 2015-02-05 | DOI: https://doi.org/10.1515/mcma-2014-0007


This paper summarizes some results of beta regression models and proposes a Bayesian method to fit these models, including joint modeling of the mean and dispersion parameters. This method is implemented through simulated and applied studies.

Keywords: Beta regression; joint mean and dispersion modeling; Bayesian method; MCMC algorithms

MSC: 62J02

About the article

Received: 2014-08-13

Accepted: 2014-12-20

Published Online: 2015-02-05

Published in Print: 2015-03-01

Citation Information: Monte Carlo Methods and Applications, Volume 21, Issue 1, Pages 49–58, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma-2014-0007.

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