A double Pólya-Gamma data augmentation scheme for a hierarchical Negative Binomial - Binomial data model

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-06-20 DOI:10.1016/j.csda.2024.108009

Xuan Ma, Jenný Brynjarsdóttir, Thomas LaFramboise

引用次数: 0

Abstract

A double Pólya-Gamma data augmentation scheme is developed for posterior sampling from a Bayesian hierarchical model of total and categorical count data. The scheme applies to a Negative Binomial - Binomial (NBB) hierarchical regression model with logit links and normal priors on regression coefficients. The approach is shown to be very efficient and in most cases out-performs the Stan program. The hierarchical modeling framework and the Pólya-Gamma data augmentation scheme are applied to human mitochondrial DNA data.

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分层负二项-二项数据模型的双 Pólya-Gamma 数据扩充方案

本文提出了一种双 Pólya-Gamma 数据扩增方案，用于从总体和分类计数数据的贝叶斯分层模型中进行后验采样。该方案适用于带有对数链接和回归系数正态先验的负二项-二项（NBB）分层回归模型。结果表明，该方法非常高效，在大多数情况下都优于 Stan 程序。分层建模框架和 Pólya-Gamma 数据增强方案被应用于人类线粒体 DNA 数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.