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Poisson Weighted Ishita Distribution: Model for Analysis of Over-Dispersed Medical Count Data

Author

Listed:
  • Para Bilal Ahmad

    (Department of Statistics, GDC Anantnag, J&K, India .)

  • Jan Tariq Rashid

    (Department of Statistics, University of Kashmir. J&K, India .)

Abstract

A new over-dispersed discrete probability model is introduced, by compounding the Poisson distribution with the weighted Ishita distribution. The statistical properties of the newly introduced distribution have been derived and discussed. Parameter estimation has been done with the application of the maximum likelihood method of estimation, followed by the Monte Carlo simulation procedure to examine the suitability of the ML estimators. In order to verify the applicability of the proposed distribution, a real-life set of data from the medical field has been analysed for modeling a count dataset representing epileptic seizure counts.

Suggested Citation

  • Para Bilal Ahmad & Jan Tariq Rashid, 2020. "Poisson Weighted Ishita Distribution: Model for Analysis of Over-Dispersed Medical Count Data," Statistics in Transition New Series, Polish Statistical Association, vol. 21(3), pages 171-184, September.
    • Handle: RePEc:vrs:stintr:v:21:y:2020:i:3:p:171-184:n:3
    DOI: 10.21307/stattrans-2020-050
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    References listed on IDEAS

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    1. Gupta, Ramesh C. & Ong, S. H., 2004. "A new generalization of the negative binomial distribution," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 287-300, March.
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