Double Generalized Beta-Binomial and Negative Binomial Regression Models

Q3 Mathematics

Revista Colombiana De Estadistica Pub Date : 2017-01-16 DOI:10.15446/RCE.V40N1.61779

Edilberto Cepeda-Cuervo, Mar'ia Victoria Cifuentes-Amado

引用次数: 3

Abstract

Overdispersion is a common phenomenon in count datasets, that can greatly affect inferences about the model. In this paper develop three joint mean and dispersion regression models in order to fit overdispersed data. These models are based on reparameterizations of the beta-binomial and negative binomial distributions. Finally, we propose a Bayesian approach to estimate the parameters of the overdispersion regression models and use it to fit a school absenteeism dataset.

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双广义β -二项和负二项回归模型

过度分散是计数数据集中的常见现象，它会极大地影响对模型的推断。为了拟合过分散的数据，本文建立了三种联合均值和离散回归模型。这些模型是基于β二项分布和负二项分布的重新参数化。最后，我们提出了一种贝叶斯方法来估计过度分散回归模型的参数，并使用它来拟合学校缺勤数据集。

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

Revista Colombiana De Estadistica STATISTICS & PROBABILITY-

CiteScore

1.20

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Colombian Journal of Statistics publishes original articles of theoretical, methodological and educational kind in any branch of Statistics. Purely theoretical papers should include illustration of the techniques presented with real data or at least simulation experiments in order to verify the usefulness of the contents presented. Informative articles of high quality methodologies or statistical techniques applied in different fields of knowledge are also considered. Only articles in English language are considered for publication. The Editorial Committee assumes that the works submitted for evaluation have not been previously published and are not being given simultaneously for publication elsewhere, and will not be without prior consent of the Committee, unless, as a result of the assessment, decides not publish in the journal. It is further assumed that when the authors deliver a document for publication in the Colombian Journal of Statistics, they know the above conditions and agree with them.