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Activity Number: 395 - Recent Advances in Zero-Inflated Regression Models
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #323833 View Presentation
Title: Bayesian Multiple-Inflation Poisson Regression for Infection Data
Author(s): Duchwan Ryu* and Devrim Bilgili and Önder Ergönül and Nader Ebrahimi
Companies: Northern Illinois University and University of North Florida and Koc ? University, Istanbul, Turkey and Northern Illinois University
Keywords: Bayesian Generalized Linear Model ; Excessive Count Response ; Zero Inflated Poisson Model
Abstract:

We propose a multiple inflated Poisson regression to model count responses containing excessive frequencies at more than one non-negative integer values in the presence of covariates. The well-known zero-inflated Poisson regression combines binary regression and Poisson regression for an excess of zero responses. To handle multiple excessive count responses we generalize the zero-inflated Poisson regression by replacing the binary regression to the multinomial regression. We discuss the properties of multiple inflated Poisson model along with regression models when some covariates are available, and use Bayesian computations for the complicated model estimations. As an application, in the study of infectious diseases which remain one of the greatest threats to human health, we observe excessive zeros and ones in the number of changes of infections and utilize the multiple inflated Poisson regressions.


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