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