Abstract
This paper proposes new parameterizations of the beta and beta binomial distributions as functions of the mean and variance parameters. From these new parameterizations, new beta and beta binomial linear regression models are formulated by assuming that appropriate real functions of the mean and variance follow linear regression structures. These models were fitted to real datasets by applying Bayesian methods, using the OpenBUGS software. The new beta regression models were fitted to the Dyslexia Reading Accuracy dataset and the new beta binomial regression models were applied to the School Absenteeism Dataset. In both cases, the results obtained by fitting these models were compared with those obtained by fitting the usual mean and dispersion beta regression models and the mean and dispersion beta binomial regression models, respectively.
Publisher
Universidad Nacional de Colombia
Reference17 articles.
1. Atkinson, A. B. (1970), 'On the measurement of inequality', Journal of Economic Theory 3(32), 244-263.
2. Cepeda-Cuervo, E. (2001), Modelagem da Variabilidade em Modelos Lineares Generalizados, Unpublished Ph.D. thesis, Mathematics Institute, Universidade Federal do Rio de Janeiro. URL: http://www.docentes.unal.edu.co/ecepedac/docs/TESISFINAL.pdf.
3. Cepeda-Cuervo, E. (2015), 'Beta regression models: Joint mean and variance modeling', Revista Colombiana de Estadística 9(1), 134-145.
4. Cepeda-Cuervo, E. & Cifuentes-Amado, M. V. (2017), 'Double generalized betabinomial and negative binomial regression models', Revista Colombiana de Estadística (40), 141-163.
5. Cepeda-Cuervo, E. & Garrido, L. (2015), 'Bayesian beta regression models with joint mean and dispersion modeling', Monte Carlo Methods and Applications 21(1), 49-58.
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