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Estimation of Generalized Poisson Distribution by the Method of Weighted Discrepancies

In: Computing Science and Statistics

Author

Listed:
  • Felix Famoye

    (Central Michigan University, Department of Mathematics)

  • Carl M.-S Lee

    (Central Michigan University, Department of Mathematics)

Abstract

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.

Suggested Citation

  • Felix Famoye & Carl M.-S Lee, 1992. "Estimation of Generalized Poisson Distribution by the Method of Weighted Discrepancies," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 332-335, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_48
    DOI: 10.1007/978-1-4612-2856-1_48
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