alexa An Empirical Study of Generalized Linear Model for Count Data | Abstract
ISSN: 2168-9679

Journal of Applied & Computational Mathematics
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This paper deals with an empirical study of generalized linear model (GLM) for count data. In particular, Poisson regression model which is also known as generalized linear model for Poisson error structure has been widely used in recent years; it is also used in modeling of count and frequency data. Quasi Poisson model was employ for handling over and under dispersion which the data was found to be over dispersed and another way of handling over dispersion is negative binomial regression model. In this study, the two regression model were compare using the Akaike information criterion (AIC), the model with minimum AIC shows the best which implies the Poisson regression model.

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Author(s): Muritala Abdulkabir, Udokang Anietie Edem, Raji Surajudeen Tunde and Bello Latifat Kemi


Poisson regression model, Quasi Poisson model, Negative binomial regression model, Smooth Complexities, Adomian Decomposition Method, Applied Mathematics, Number Theory, Sensitivity Analysis, Convection Diffusion Equations, Numerical Solutions, Nonlinear Differential Equations, Differential Transform Method , Balance Law, Quasilinear Hyperbolic Systems, Mixed Initial-boundary Value, Fuzzy Boundary Value, Semi Analytical-Solution, Integrated Analysis, Fuzzy Environments, Molecular Modelling, Fuzzy Quasi-Metric Space, Three Dimensional Steady State, Computational Model

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