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Estimation of Generalized Poisson Distribution by the Method of Weighted Discrepancies

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Computing Science and Statistics

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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.

The first author gratefully acknowledges the support received from the Summer Fellowship and Research Professorship Program at Central Michigan University under Grant #42126.

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References

  1. Consul, P. C. (1989): Generalized Poisson Distributions-Properties and Applications. Marcel Dekker, Inc., New York.

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  2. Consul, P. C. and Jain, G. C. (1973): “A generalization of the Poisson distribution.” Technometrics, 15, 5–8.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Central Michigan University, Mt. Pleasant, MI, 48859, USA

    Felix Famoye & Carl M.-S Lee

Authors
  1. Felix Famoye
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  2. Carl M.-S Lee
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Editor information

Editors and Affiliations

  1. Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA

    Connie Page  & Raoul LePage  & 

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© 1992 Springer-Verlag New York, Inc.

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Famoye, F., Lee, C.MS. (1992). Estimation of Generalized Poisson Distribution by the Method of Weighted Discrepancies. In: Page, C., LePage, R. (eds) Computing Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2856-1_48

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  • DOI: https://doi.org/10.1007/978-1-4612-2856-1_48

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97719-5

  • Online ISBN: 978-1-4612-2856-1

  • eBook Packages: Springer Book Archive

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