Estimation of Generalized Poisson Distribution by the Method of Weighted Discrepancies

  • Felix Famoye
  • Carl M.-S Lee

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.

Keywords

Marketing 

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References

  1. 1.
    Consul, P. C. (1989): Generalized Poisson Distributions-Properties and Applications. Marcel Dekker, Inc., New York.MATHGoogle Scholar
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    Consul, P. C. and Jain, G. C. (1973): “A generalization of the Poisson distribution.” Technometrics, 15, 5–8.MathSciNetCrossRefGoogle Scholar
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    Consul, P. C. and Shoukri, M. M. (1985): “The generalized Poisson distribution when the sample mean is larger than the sample variance.” Com. Statist. Simulation Comput., 14(3), 667–681.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 1992

Authors and Affiliations

  • Felix Famoye
    • 1
  • Carl M.-S Lee
    • 1
  1. 1.Department of MathematicsCentral Michigan UniversityMt. PleasantUSA

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