A Bayesian approach to errors-in-variables beta regression

被引：2

作者：

Figueroa-Zuniga, Jorge ^{[1
]}

Carrasco, Jalmar M. F. ^{[2
]}

Arellano-Valle, Reinaldo ^{[3
]}

Ferrari, Silvia L. P. ^{[4
]}

机构：

[1] Univ Concepcion, Dept Stat, Concepcion, Chile

[2] Univ Fed Bahia, Dept Stat, Salvador, BA, Brazil

[3] Pontificia Univ Catolica Chile, Dept Stat, Santiago, Chile

[4] Univ Sao Paulo, Dept Stat, Sao Paulo, Brazil

来源：

BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS | 2018年 / 32卷 / 03期

基金：

巴西圣保罗研究基金会;

关键词：

Bayesian analysis; beta distribution; beta regression; continuous proportions; errors-in-variables models;

D O I：

10.1214/17-BJPS354

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：559 / 582

页数：24

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