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Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data

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Abstract & Overview

The generalized Poisson regression model has been used to model dispersed count data. It is a good competitor to the negative binomial regression model when the count data is over-dispersed. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for the situations where the data generating process results into too many zeros. In this paper, we propose a zero-inflated generalized Poisson (ZIGP) regression model to model domestic violence data with too many zeros. Estimation of the model parameters using the method of maximum likelihood is provided. A score test is presented to test whether the number of zeros is too large for the generalized Poisson model to adequately fit the domestic violence data

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Felix Famoye, Karan P. Singh (2021). Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data. Journal of Data Science, 4(1), 117–130. https://doi.org/10.6339/jds.2006.04(1).257

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Famoye, F., & Singh, K. P. (2021). Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data. Journal of Data Science, 4(1), 117–130. https://doi.org/10.6339/jds.2006.04(1).257
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