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A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2020-12-03 , DOI: 10.1080/02664763.2020.1854203
Zahra Barkhordar 1 , Mohsen Maleki 2 , Zahra Khodadadi 1 , Darren Wraith 3 , Farajollah Negahdari 4
Affiliation  

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data.



中文翻译:

正常同方差非线性回归模型的两片尺度混合的贝叶斯方法

在本应用说明文件中,我们提出并检验了贝叶斯方法在同方差非线性回归 ( NLR ) 模型中的性能,假设误差与正态 ( TP - SMN ) 分布的两部分比例混合。TP - SMN是一个大的分布族,涵盖了对称/非对称分布以及轻/重尾分布,并提供了另一个众所周知的分布族的替代方案,称为斜正态分布的尺度混合。提出的族和贝叶斯方法为NLR提供了相当大的灵活性和优势在不同的实际环境中建模。我们使用模拟和真实数据检查该方法的性能。

更新日期:2020-12-03
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