Brazilian Journal of Probability and Statistics

Bayesian analysis of multiple-inflation Poisson models and its application to infection data

Duchwan Ryu, Devrim Bilgili, Önder Ergönül, and Nader Ebrahimi

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In this article we propose a multiple-inflation Poisson regression to model count response data containing excessive frequencies at more than one non-negative integer values. To handle multiple excessive count responses, we generalize the zero-inflated Poisson regression by replacing its binary regression with the multinomial regression, while Su et al. [Statist. Sinica 23 (2013) 1071–1090] proposed a multiple-inflation Poisson model for consecutive count responses with excessive frequencies. We give several properties of our proposed model, and do statistical inference under the fully Bayesian framework. We perform simulation studies and also analyze the data related to the number of infections collected in five major hospitals in Turkey, using our methodology.

Article information

Braz. J. Probab. Stat., Volume 32, Number 2 (2018), 239-261.

Received: March 2016
Accepted: October 2016
First available in Project Euclid: 17 April 2018

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Zentralblatt MATH identifier

Bayesian generalized linear model EM algorithm excessive count response likelihood function zero-inflated poisson model


Ryu, Duchwan; Bilgili, Devrim; Ergönül, Önder; Ebrahimi, Nader. Bayesian analysis of multiple-inflation Poisson models and its application to infection data. Braz. J. Probab. Stat. 32 (2018), no. 2, 239--261. doi:10.1214/16-BJPS340.

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