Bayesian inference for the negative binomial-Sushila linear model
type: Item
instance of: scholarly article
title: Bayesian inference for the negative binomial-Sushila linear model
zbMATH Open document ID: 1425.62090
DOI: 10.1134/S1995080219010141
author: Darika Yamrubboon
author: Ampai Thongteeraparp
author: Winai Bodhisuwan
author: K. Jampachaisri
author: Andrei Volodin
published in: Lobachevskii Journal of Mathematics
publication date: 2019-07-15T00:00:00Z
review text: This article explores the use of the negative binomial-Sushila (NB-S) distribution in generalized linear models to develop a NB-S linear model for count data. New parametrizations of the distribution are employed. The model parameters are estimated using a Bayesian approach. The comparison of generalized linear models based on Poisson, negative binomial and NB-S distributions to two real data sets with over-dispersion is presented, considering the deviance, the deviance information criterion and the effective number of parameters for model comparison. The NB-S model seems to outperform the other models in this study.
reviewed by: Annibal Parracho Sant'Anna
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 7080617
zbMATH Keywords: count data
zbMATH Keywords: negative binomial-Sushila distribution
zbMATH Keywords: generalized linear model
zbMATH Keywords: over-dispersion
zbMATH Keywords: Bayesian approach
describes a project that uses: R
describes a project that uses: COUNT
describes a project that uses: WinBUGS
describes a project that uses: BayesDA
describes a project that uses: R2jags
MaRDI profile type: Publication
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full work available at URL: s1995080219010141
OpenAlex ID: W2935992724
description: scientific article; zbMATH DE number 7080617