Mixed beta regression: A Bayesian perspective
Mixed beta regression: A Bayesian perspective
复制标题
DOI：
10.1016/j.csda.2012.12.002
复制
发表时间：
2013-05-01
影响因子：
1.8
通讯作者：
Ferrari, Silvia L. P.
中科院分区：
文献类型：
--
作者：
Figueroa-Zuniga, Jorge I.;Arellano-Valle, Reinaldo B.;Ferrari, Silvia L. P.
本文以最近的研究为基础，重点关注连续有界数据的回归建模，例如连续尺度上测量的比例。具体来说，它处理具有贝叶斯方法混合效应的 beta 回归模型。我们根据其均值和精度参数对贝塔定律进行适当的参数化，并允许通过可能涉及固定和随机效应的回归结构对这两个参数进行建模。讨论了先验分布的规范，提供了通过吉布斯采样的计算实现，并给出了说明性示例。 (C) 2012 Elsevier B.V. 保留所有权利。
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. (C) 2012 Elsevier B.V. All rights reserved.