Bayesian Inference in the Noncentral Student-t Model
Bayesian Inference in the Noncentral Student-t Model
复制标题
非中心 Student-t 模型中的贝叶斯推理
DOI：
10.1198/106186002317375695
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
E. Tsionas
中科院分区：
文献类型：
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作者：
E. Tsionas
本文讨论了非中心Student-t分布干扰下线性模型的贝叶斯推断。当长尾和不对称都是数据的特征时，分布是有用的。该分布可以表示为具有根据卡方分布的逆权重的法线的位置-尺度混合。计算使用吉布斯采样与数据增强。标准普尔股票收益率的实证研究表明，后验概率非常有利于非中心的学生t规范的对称对应。
This article takes up Bayesian inference in linear models with disturbances from a noncentral Student-t distribution. The distribution is useful when both long tails and asymmetry are features of the data. The distribution can be expressed as a location-scale mixture of normals with inverse weights distributed according to a chi-square distribution. The computations are performed using Gibbs sampling with data augmentation. An empirical application to Standard and Poor's stock returns indicates that posterior odds strongly favor a noncentral Student-t specification over its symmetric counterpart.