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August 2018 A Bayesian approach to errors-in-variables beta regression
Jorge Figueroa-Zúñiga, Jalmar M. F. Carrasco, Reinaldo Arellano-Valle, Silvia L. P. Ferrari
Braz. J. Probab. Stat. 32(3): 559-582 (August 2018). DOI: 10.1214/17-BJPS354

Abstract

Beta regression models have been widely used for the analysis of limited-range continuous variables. Here, we consider an extension of the beta regression models that allows for explanatory variables to be measured with error. Then we propose a Bayesian treatment for errors-in-variables beta regression models. The specification of prior distributions is discussed, computational implementation via Gibbs sampling is provided, and two real data applications are presented. Additionally, Monte Carlo simulations are used to evaluate the performance of the proposed approach.

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Jorge Figueroa-Zúñiga. Jalmar M. F. Carrasco. Reinaldo Arellano-Valle. Silvia L. P. Ferrari. "A Bayesian approach to errors-in-variables beta regression." Braz. J. Probab. Stat. 32 (3) 559 - 582, August 2018. https://doi.org/10.1214/17-BJPS354

Information

Received: 1 May 2016; Accepted: 1 January 2017; Published: August 2018
First available in Project Euclid: 8 June 2018

zbMATH: 06930039
MathSciNet: MR3812382
Digital Object Identifier: 10.1214/17-BJPS354

Keywords: Bayesian analysis , Beta distribution , Beta regression , continuous proportions , errors-in-variables models

Rights: Copyright © 2018 Brazilian Statistical Association

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Vol.32 • No. 3 • August 2018
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