Likelihood inference for Type I bivariate Pólya–Aeppli distribution

ABSTRACT

This article discusses likelihood inference for the Type I bivariate Pólya–Aeppli distribution. The Type I bivariate Pólya–Aeppli distribution was derived by Minkova and Balakrishnan by using compounding with geometric random variables and the trivariate reduction method. They also discussed the moment estimation of the parameters of the Type I bivariate Pólya–Aeppli distribution. Here, we carry out a simulation study to compare the performance of the developed Maximum Likelihood Estimation (MLE) method with the moment estimation. The obtained results show that, through the MLEs require more computational time compared to the moment estimates (MoM), the MLEs perform better, in most of the settings, than the MoM. Finally, we apply the Type I bivariate Pólya–Aeppli model to a real dataset containing the frequencies of railway accidents in two subsequent six-year periods for the purpose of illustration. We also carry out some hypothesis tests using the Wald test statistic. From these results, we conclude that the two variables belong to the same univariate Pólya–Aeppli distribution, but are correlated.

KEYWORDS:

• Confidence intervals
• Fisher information
• Maximum likelihood method
• Method of Moments
• Newton-Raphson method
• Type I bivariate Pólya–Aeppli distribution

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62F10
• Secondary 5C60