Estimation of Generalized Poisson Distribution by the Method of Weighted Discrepancies
The generalized Poisson distribution (GPD) has been found to be a very versatile discrete distribution with applications in various areas of study such as engineering, manufacturing, survival analysis, genetic and branching processes. In this paper, we study the estimation of generalized Poisson distribution by the method of weighted discrepancies between observed and expected frequencies. The methods of maximum likelihood, minimum chi-square and the minimum discrimination information estimation are special cases of the weighted discrepancies method. It is found that the weighted discrepancies method is better than the minimum chi-square method and compares very well with the maximum likelihood method.
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