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Multivariate Beta Regression with Application in Small Area Estimation


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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.10.1201/9781482296426Search 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/b14835Search 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.10.1002/9781118735855Search 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

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Mathematics, Probability and Statistics