[Akaike, H. 1973. “Information Theory and an Extension of the Maximum Likelihood Principle.” In Second International Symposium on Information Theory, edited by B.N. Petrov and F. Csaki, 267–281. Budapest: Akademiai Kiado.]Search in Google Scholar
[Bartels, R. and D.G. Fiebig. 1991. “A Simple Characterization of Seemingly Unrelated Regressions Models in Which OLS is Blue.” The American Statistician 45: 137–140. Doi: http://dx.doi.org/10.2307/2684378.]Search in Google Scholar
[Branscum, A.J., W.O. Johnson, and M.C. Thurmond. 2007. “Bayesian Beta Regression: Applications to Household Expenditure Data and Genetic Distance Between Foot-and-Mouth Disease Viruses.” Australian & New Zealand Journal of Statistics 49: 287–301. Doi: http://dx.doi.org/10.1111/j.1467842X.2007.00481.x.]Search in Google Scholar
[Cepeda-Cuervo, E., J.A. Achcar, and L.G. Lopera. 2014. “Bivariate Beta Regression Models: Joint Modeling of the Mean, Dispersion and Association Parameters.” Journal of Applied Statistics 41: 677–687. Doi: http://dx.doi.org/10.1080/02664763.2013.847071.]Search in Google Scholar
[Da-Silva, C.Q., H.S. Migon, and L.T. Correia. 2011. “Dynamic Bayesian Beta Models.” Computational Statistics and Data Analysis 55: 2074–2089. Doi: http://dx.doi.org/10.1016/j.csda.2010.12.011.]Search in Google Scholar
[Datta, G.S., B. Day, and I. Basawa. 1999. “Empirical Best Linear Unbiased and Empirical Bayes Prediction in Multivariate Small Area Estimation.” Journal of Statistical Planning and Inference 75: 269–279. Doi: http://dx.doi.org/10.1016/S0378-3758(98)00147-5.]Search in Google Scholar
[Doornik, J.A. 2007. Object-Oriented Matrix Programming Using Ox, 3 ed. London: Timberlake Consultants Press.]Search in Google Scholar
[Fabrizi, E., M.R. Ferrante, S. Pacei, and C. Trivisano. 2011. “Hierarchical Bayes Multivariate Estimation of Poverty Rates Based on Increasing Thresholds for Small Domains.” Computational Statistics and Data Analysis 55: 1736–1747. Doi: http://dx.doi.org/10.1016/j.csda.2010.11.001.]Search in Google Scholar
[Fay, R.E. 1987. “Application of Multivariate Regression to Small Domain Estimation.” In Small Area Statistics, edited by R. Platek, J. Rao, C. Särndal, and M. Singh, 91–102. New York: Wiley.]Search in Google Scholar
[Ferrari, S.L.P. and F. Cribari-Neto. 2004. “Beta Regression for Modelling Rates and Proportions.” Journal of Applied Statistics 31: 799–815. Doi: http://dx.doi.org/10.1080/0266476042000214501.]Search in Google Scholar
[Gamerman, D. and H.F. Lopes. 2006. Markov Chain Monte Carlo: Stochastic simulation for Bayesian inference, 2 ed. London: Chapman & Hall.10.1201/9781482296426]Search in Google Scholar
[Gelman, A. (2006). “Prior Distributions for Variance Parameters in Hierarchical Models.” Bayesian Analysis 1: 515–534. Doi: http://dx.doi.org/10.1214/06-BA117A.]Search in Google Scholar
[Gilks, W. and G. Roberts. 1996. “Strategies for Improving mcmc.” In Markov Chain Monte Carlo in Practice, edited by S.R.W. Gilks and D. Spiegelhalter, 89–114. London: Chapman & Hall.10.1201/b14835]Search in Google Scholar
[Gueorguieva, R.V. and A. Agresti. 2001. “A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses.” Journal of the American Statistical Association 96: 1102–1112. Doi: http://dx.doi.org/10.1198/016214501753208762.]Search in Google Scholar
[Huard, D., G. Évin, and A.-C. Favre. 2006. “Bayesian Copula Selection.” Computational Statistics and Data Analysis 51: 809–822. Doi: http://dx.doi.org/10.1016/j.csda.2005.08.010.]Search in Google Scholar
[Jiang, J. 2007. Linear and Generalized Linear Mixed Models and Their Applications. Springer Series in Statistics. New York: Springer.]Search in Google Scholar
[Liu, B., P. Lahiri, and G. Kalton. 2014. “Hierarchical Bayes Modeling of Survey-Weighted Small Area Proportions.” Survey Methodology 40: 1–13. Available at: http://www.statcan.gc.ca/pub/12-001-x/2014001/article/14030-eng.pdf (accessed 1 December 2016).]Search in Google Scholar
[Lohr, S.L. 1999. Sampling: design and analysis, 1st ed. Place of publication: Brooks/Cole Publishing Company, California, USA.]Search in Google Scholar
[Melo, T.F., K.L. Vasconcellos, and A.J. Lemonte. 2009. “Some Restriction Tests in a New Class of Regression Models for Proportions.” Computational Statistics and Data Analysis 53: 3972–3979. Doi: http://dx.doi.org/10.1016/j.csda.2009.06.005.]Search in Google Scholar
[Murteira, J.M.R. and J.J.S. Ramalho. 2014. “Regression Analysis of Multivariate Fractional Data.” Econometric Reviews 0: 1–38. Doi: http://dx.doi.org/10.1080/07474938.2013.806849.]Search in Google Scholar
[Neal, R.M. (2003). “Slice Sampling.” The Annals of Statistics 31: 705–767. Doi: http://dx.doi.org/10.1214/aos/1056562461.]Search in Google Scholar
[Nelsen, R.B. 2006. An Introduction to Copulas, 2 ed. New York: Springer.]Search in Google Scholar
[Olkin, I. and R. Liu. 2003. “A Bivariate Beta Distribution.” Statistics & Probability Letters 62: 407–412. Doi: http://dx.doi.org/10.1016/S0167-7152(03)00048-8.]Search in Google Scholar
[Ospina, R. and S.L. Ferrari. 2012. “A General Class of Zero-or-One Inflated Beta Regression Models.” Computational Statistics and Data Analysis 56: 1609–1623. Doi: http://dx.doi.org/10.1016/j.csda.2011.10.005.]Search in Google Scholar
[Pfeffermann, D., F.A. da Silva Moura, and P.L. do Nascimento Silva. 2006. “Multi-Level Modelling Under Informative Sampling.” Biometrika 93: 943–959. Doi: http://dx.doi.org/10.1093/biomet/93.4.943.]Search in Google Scholar
[Rao, J.N.K. and I. Molina. 2015. Small area estimation, 2nd ed. Hoboken, New Jersey: Wiley.10.1002/9781118735855]Search in Google Scholar
[Schwarz, G. (1978). “Estimating the Dimension of a Model.” The Annals of Statistics 6: 461–464. Doi: http://dx.doi.org/10.1214/aos/1176344136.]Search in Google Scholar
[Silva, R.S. and H.F. Lopes. 2008. “Copula, Marginal Distributions and Model Selection: a Bayesian Note.” Statistics and Computing 18: 313–320. Doi: http://dx.doi.org/10.1007/s11222-008-9058-y.]Search in Google Scholar
[Simas, A.B., W. Barreto-Souza, and A.V. Rocha. 2010. “Improved Estimators for a General Class of Beta Regression Models.” Computational Statistics and Data Analysis 54: 348–366. Doi: http://dx.doi.org/10.1016/j.csda.2009.08.017.]Search in Google Scholar
[Smithson, M. and J. Verkuilen. 2006. “A Better Lemon-Squeezer? Maximum Likelihood Regression With Beta-Distributed Dependent Variables.” Psychological Methods 11: 54–71. Doi: http://dx.doi.org/10.1037/1082-989X.11.1.54.]Search in Google Scholar
[Souza, D.F. 2011. “Regressão Beta Multivariada com Aplicaçôes em Pequenas Áreas.” Ph.D. thesis, Instituto de Matemática da Universidade Federal do Rio de Janeiro. Available at: http://www.pg.im.ufrj.br/teses/Estatistica/Doutorado/018.pdf (accessed 1 December 2016).]Search in Google Scholar
[Spiegelhalter, D.J., N.G. Best, B.P. Carlin, and A.V.D. Linde. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64: 583–639. Doi: http://dx.doi.org/10.1111/1467-9868.00353.]Search in Google Scholar
[Teixeira-Pinto, A. and S.-L. T. Normand. 2009. “Correlated Bivariate Continuous and Binary Outcomes: Issues and Applications.” Statistics in Medicine 28: 1753–1773. Doi: http://dx.doi.org/10.1002/sim.3588.]Search in Google Scholar