Adaptive Regression Model for Highly Skewed Count Data.
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Date
2019-01-11
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Publisher
IAEME
Abstract
A big task often faced by practitioners is in deciding the appropriate model to adopt
in fitting count datasets. This paper is aimed at investigating a suitable model for fitting
highly skewed count datasets. Among other models, COM-Poisson regression model was
proposed in this paper for fitting count data due to its varying normalizing constant. Some
statistical models were investigated along with the proposed model; these include
Poisson, Negative Binomial, Zero-Inflated, Zero-inflated Poisson and Quasi- Poisson
models. A real life dataset relating to visits to Doctor within a given period was equally
used to test the behavior of the underlying models. From the findings, it is recommended
that COM-Poisson regression model should be adopted in fitting highly skewed count
datasets irrespective of the type of dispersion.