Derivation of Poisson Xrama Distribution and Its Properties

This study introduces the Poisson Xrama Distribution, a model for analyzing count data that exhibits overdispersion. By combining the Poisson distribution with the Xrama distribution, this model addresses the limitations of traditional Poisson models, which assume equidispersion. The Poisson Xrama Distribution offers enhanced flexibility in handling variance inflation, making it suitable for scenarios where standard Poisson models are insufficient. Key statistical properties, including moments, variance, skewness, kurtosis and index of dispersion measures are derived. Maximum likelihood estimation is employed for parameter estimation, providing a robust framework for practical applications. This distribution is particularly useful in fields where count data often display overdispersion, such as biology and economics, offering a promising alternative to existing distribution models.

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Publication Details

Journal
Asian Journal of Science Technology Engineering and Art
Published
2025-04-02
DOI
https://doi.org/10.58578/ajstea.v3i3.5294
Primary Topic
Bayesian Methods and Mixture Models
Type
article
Field-Weighted Citation Impact
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article

Derivation of Poisson Xrama Distribution and Its Properties

Sauta Saidu Abdulkadir, Danjuma Jibasen, Adesupo A. Akinrefon, Bamigbala Olateju Alao
Asian Journal of Science Technology Engineering and Art
Bayesian Methods and Mixture Models
article

Derivation of Poisson Xrama Distribution and Its Properties

Sauta Saidu Abdulkadir, Danjuma Jibasen, Adesupo A. Akinrefon, Bamigbala Olateju Alao
article en

Abstract

This study introduces the Poisson Xrama Distribution, a model for analyzing count data that exhibits overdispersion. By combining the Poisson distribution with the Xrama distribution, this model addresses the limitations of traditional Poisson models, which assume equidispersion. The Poisson Xrama Distribution offers enhanced flexibility in handling variance inflation, making it suitable for scenarios where standard Poisson models are insufficient. Key statistical properties, including moments, variance, skewness, kurtosis and index of dispersion measures are derived. Maximum likelihood estimation is employed for parameter estimation, providing a robust framework for practical applications. This distribution is particularly useful in fields where count data often display overdispersion, such as biology and economics, offering a promising alternative to existing distribution models.

Asian Journal of Science Technology Engineering and ArtVol. 3(3)
Kwararafa University (NG), Federal University Wukari, Modibbo Adama University of Technology (NG)
Openalex Percentile: Top 97%
Bayesian Methods and Mixture Models
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