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Mixed beta regression: A Bayesian perspective

Author

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  • Figueroa-Zúñiga, Jorge I.
  • Arellano-Valle, Reinaldo B.
  • Ferrari, Silvia L.P.

Abstract

This paper builds on recent research that focuses on regression modeling of continuous bounded data, such as proportions measured on a continuous scale. Specifically, it deals with beta regression models with mixed effects from a Bayesian approach. We use a suitable parameterization of the beta law in terms of its mean and a precision parameter, and allow both parameters to be modeled through regression structures that may involve fixed and random effects. Specification of prior distributions is discussed, computational implementation via Gibbs sampling is provided, and illustrative examples are presented.

Suggested Citation

  • Figueroa-Zúñiga, Jorge I. & Arellano-Valle, Reinaldo B. & Ferrari, Silvia L.P., 2013. "Mixed beta regression: A Bayesian perspective," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 137-147.
    • Handle: RePEc:eee:csdana:v:61:y:2013:i:c:p:137-147
    DOI: 10.1016/j.csda.2012.12.002
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    11. Francesco Giovinazzi & Daniela Cocchi, 2022. "Social Integration of Second Generation Students in the Italian School System," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 287-307, February.
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    14. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    15. Fernanda L. Schumacher & Larissa A. Matos & Víctor H. Lachos & Carlos A. Abanto-Valle & Luis M. Castro, 2024. "A Censored Time Series Analysis for Responses on the Unit Interval: An Application to Acid Rain Modeling," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 637-660, February.
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