Lobachevskii Journal of Mathematics

, Volume 40, Issue 1, pp 42–54 | Cite as

Bayesian Inference for the Negative Binomial-Sushila Linear Model

  • Darika YamrubboonEmail author
  • Ampai ThongteeraparpEmail author
  • Winai BodhisuwanEmail author
  • Katechan JampachaisriEmail author
  • Andrei VolodinEmail author


The aim of this article is to develop a new linear model for count data. The main idea is in an application of a new generalized linear model framework, which we call the Negative Binomial-Sushila linear model. The Negative Binomial-Sushila distribution has been proposed recently and applied to count data. This distribution is constructed as a mixture of the Negative Binomial and Sushila distributions. The mixed distribution is a flexible alternative to the Poisson distribution when over-dispersed count data is analyzed. The parameters of this distribution are estimated using a Bayesian approach with R2jags package of the R language. The Negative Binomial-Sushila linear model is applied to fit two real data sets with an over-dispersion and its performance is compared with the performance of some traditional models. The results show that the Negative Binomial-Sushila generalized linear model fits the data sets better than the traditional generalized models for these data sets.

Keywords and phrases

count data Negative Binomial-Sushila distribution generalized linear model over-dispersion Bayesian approach 


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Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Department of StatisticsKasetsart UniversityBangkokThailand
  2. 2.Department of MathematicsNaresuan UniversityPhitsanulokThailand
  3. 3.Department of Mathematics and StatisticsUniversity of ReginaReginaCanada

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