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Multivariate Beta Regression with Application in Small Area Estimation

Author

Listed:
  • Souza Debora F.

    (Coordenação de Métodos e Qualidade, Instituto Brasileiro de Geografia e Estatística (IBGE). Rio de Janeiro, Brazil.)

  • Moura Fernando A. S.

    (IM-UFRJ – Statistics Department, Rio de Janeiro, Rio de Janeiro, Brazil.)

Abstract

Multivariate beta regression models for jointly modelling two or more variables whose values belong in the (0,1) interval, such as indexes, rates or proportions, are proposed for making small area predictions. The multivariate model can help the estimation process by borrowing strength between units and obtaining more precise estimates, especially for small samples. Each response variable is assumed to have a beta distribution so the models could accommodate multivariate asymmetric data. Copula functions are used to construct the joint distribution of the dependent variables; all the marginal distributions are fixed as beta. A hierarchical beta regression model is additionally proposed with correlated random effects. We present an illustration of the proposed approach by estimating two indexes of educational attainment at school level in a Brazilian state. Our predictions are compared with separate univariate beta regressions. The inference process was conducted using a full Bayesian approach.

Suggested Citation

  • Souza Debora F. & Moura Fernando A. S., 2016. "Multivariate Beta Regression with Application in Small Area Estimation," Journal of Official Statistics, Sciendo, vol. 32(3), pages 747-768, September.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:3:p:747-768:n:10
    DOI: 10.1515/jos-2016-0038
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    References listed on IDEAS

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    3. Simas, Alexandre B. & Barreto-Souza, Wagner & Rocha, Andréa V., 2010. "Improved estimators for a general class of beta regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 348-366, February.
    4. Edilberto Cepeda-Cuervo & Jorge Alberto Achcar & Liliana Garrido Lopera, 2014. "Bivariate beta regression models: joint modeling of the mean, dispersion and association parameters," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 677-687, March.
    5. Fabrizi, Enrico & Ferrante, Maria Rosaria & Pacei, Silvia & Trivisano, Carlo, 2011. "Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1736-1747, April.
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