An Application Comparison of Two Poisson Models on Zero Count Data

Abstract

Counting data (including zero counts) appear in a variety of applications, so counting models have become popular in many fields. In statistical fields, count data can be defined as observation types that use only non-negative integer values. Sometimes researchers may Counts more zeros than the expected. You may describe Excess zero as Zero-Inflation, excess zeros cause over-dispersion. So, the objective of this paper is use zero-inflated regression models (Poisson Regression model, Zero-Inflated Poisson (ZIP), and Zero-Altered Poisson (ZAP)) to analyse rainfall data and select the best model that deal with these type of data. It has been shown through the study and practical application that the advantage and quality of the Zero-Altered Poisson Regression (ZAPR) where the Zero-Altered Poisson regression model was the best count data model for our data, Although it is hard to distinguish Zero-Inflated Poisson (ZIP) regression model, it is better than Poisson regression model.

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