**Open Journal of Statistics**

Vol.05 No.02(2015), Article ID:55814,2 pages

10.4236/ojs.2015.52017

A Note on the Characterization of Zero-Inflated Poisson Model

G. Nanjundan, Sadiq Pasha

Department of Statistics, Bangalore University, Bangalore, India

Email: nanzundan@gmail.com

Copyright © 2015 by authors and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY).

Received 14 March 2015; accepted 16 April 2015; published 20 April 2015

ABSTRACT

Zero-Inflated Poisson model has found a wide variety of applications in recent years in statistical analyses of count data, especially in count regression models. Zero-Inflated Poisson model is characterized in this paper through a linear differential equation satisfied by its probability generating function [1] [2] .

**Keywords:**

Zero-Inflated Poisson Model, Probability Generating Function, Linear Differential Equation

1. Introduction

A random variable X is said to have a zero-inflated Poisson distribution if its probability mass function is given by

(1)

where and, ,.

Thus, the distribution of X is a mixture of a distribution degenerate at zero and a Poisson distribution with mean.

2. Probability Generating Function

The probability generating function (pgf) of X is given by

.

3. Characterization

Let X be a non-negative integer valued random variable with and the pgf. Then, the distribution of X is zero-inflated Poisson if and only if, where, b are constants and is the first derivative of.

Proof:

1) Suppose that X has a zero-inflated Poisson distribution specified in (1.1). Then the pgf of X is given by

On differentiation, we get

.

Hence satisfies the linear differential equation

(2)

2) Suppose that the pgf of X satisfies

If, then and in turn. By the property of the pgf,. But, which is not possible because.

Therefore.

3) The Linear Differential Equation

The linear differential equation is of the form

where and are functions of.

Then its solution is given by

,

where c is an arbitrary constant.

Here

.

Hence,.

Therefore the solution of the Equation (2) is given by

.

We now extract the probabilities, using the above solution.

Since is a pgf, , where is the k-th derivative of.

We get

, , , and so on.

Now,

Since, it is easy to see that,

We have

with and.

Therefore X has the pgf specified in Equation (1).

References

- Nanjundan, G. (2011) A Characterization of the Members of a Subfamily of Power Series Distributions. Applied mathematics, 2, 750-751. http://dx.doi.org/10.4236/am.2011.26099
- Nanjundan, G. and Ravindra Naika, T. (2012) An Asymptotic Comparison of the Maximum Likelihood and the Moment Estimators in a Zero-Inflated Poisson Model. Applied mathematics, 3, 610-617. http://dx.doi.org/10.4236/am.2012.36095