Derivation of Two Parameters Poisson Rani Distribution and Its Properties

Crossmark

Article Sidebar

Page Numbers: 58-70
Download PDF
Published: Dec 19, 2024
Digital Object Identifier: 10.58578/mjms.v3i1.4385
Save this to:
Save to Mendeley Download RIS

Article Metrics:
Viewed: 243 times
Downloaded: 215 times
Article can trace at:

Author Fee:
Free Publication Fees for Foreign Researchers (USD 0.00)
Check for article on SINTA:

LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers

Connected Papers:
Connected Papers


Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.


Authors:
Bamigbala Olateju Alao1*, Pantuvo Tsoke Peter2, Ikrimat Aliyu Babando3, Abdulganiy Abdullahi Gatta4
Copyright :

Authors retain copyright and grant the journal right of first publication.

Explore Our Journals
Find the journal that best fits your research. If this journal does not match your manuscript’s scope, explore our portfolio below to select a more suitable publication venue.

Main Article Content

Bamigbala Olateju Alao
Federal University Wukari, Taraba State, Nigeria
Pantuvo Tsoke Peter
Federal University Wukari, Taraba State, Nigeria
Ikrimat Aliyu Babando
College of Education, Zing, Taraba State, Nigeria
Abdulganiy Abdullahi Gatta
Modibbo Adama University, Yola, Adamawa State, Nigeria

Abstract

This study introduces the Two Parameters Poisson Rani Distribution (TPPRD). The probability distribution of TPPRD is derived by assuming that the parameters of the Poisson distribution follow the Two Parameters Rani Distribution, resulting in the formation of the TPPRD. The study derives some of its fundamental properties and demonstrates that TPPRD is a special-case distribution capable of handling overdispersed count data. Additionally, the maximum likelihood estimators are used to derive equations for estimating the parameters of the Two Parameters Poisson Rani Distribution.

Keywords:
Share Article:

Citation Metrics:

Scopus

Downloads

Download data is not yet available.

Citation Metrics & Similar Scopus Articles

Data source Crossref
0
citations
Citation counts are source-specific and may differ because database coverage, reference matching, and update schedules are different. Counts are not added together. Crossref values represent citation links registered and matched by Crossref.
Check Secondary Documents in Scopus
Open this article in Scopus, then check the Secondary documents tab. Use Manual Citation Fallback only for counts you have verified manually.
Open in Scopus
Similar Scopus Articles
Scopus
  1. Urazkeldiyeva D.A. (2027)
    Development of technology for obtaining high-purity sodium chloride with induced impurity removal and process modeling
    Kompleksnoe Ispolzovanie Mineralnogo Syra, 342(3), 56-64
  2. Kholov K.I. (2027)
    Mineralogical features and optimization of combined beneficiation flowsheets for refractory gold-bearing ores of the Pakrut deposit (Central Tajikistan)
    Kompleksnoe Ispolzovanie Mineralnogo Syra, 342(3), 16-26
  3. Hassanalizadeh S. (2027)
    Phonological and Morphological Changes of Persian Loanwords in Ottoman Turkish Language in the Process of Localization
    Language Related Research, 17(4), 433-467

Article Details

How to Cite
Alao, B. O., Peter, P. T., Babando, I. A., & Gatta, A. A. (2024). Derivation of Two Parameters Poisson Rani Distribution and Its Properties. Mikailalsys Journal of Mathematics and Statistics, 3(1), 58-70. https://doi.org/10.58578/mjms.v3i1.4385

Creative Commons License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

References

Ahmad A. H. & Amjad D. A. (2021). New Distribution for Fitting Discrete Data: The Poisson-Gold Distribution and Its Statistical Properties. Austrian Journal of Statistics. Vol. 50, pp. 19-35.

Emrah A. (2018). A new zero-inflated regression model with application. Journal of Statisticians: Statistics and Actuarial Sciences. 2, 73-80. www.istatistikciler.org.

Lawal, B. H. (2011). On the Negative Binomial Generalized Exponential Distribution and its Applications. Applied Mathematical Sciences, Vol. 11, No. 8, pp. 345-360. https://doi.org/10.12988/ams.2017.612288.

Maya, R., Irshad, M.R., Chesneau, C., Nitin, S.L. & Shibu, D.S. (2022). On Discrete Poisson–Mirra Distribution: Regression, INAR(1) Process and Applications. Axioms, 11, 193, pp. 1-27. https://doi.org/ 10.3390/axioms11050193.

Mohamed, S. E., Muhammad, A., Amani, A., Afrah, A., & Mahmoud E. (2023). Discrete Extension of Poisson Distribution for Overdispersed Count Data: Theory and Applications. Journal of Mathematics, Vol. 2023, pp. 1-15. https://doi.org/10.1155/2023/2779120.

Noemi, C. & Cinzia V. (2022). Dealing with overdispersion in multivariate count data. Computational Statistics & Data Analysis. Vol. 170, Page: 107447. https://doi.org/10.1016/j.csda.2022.107447.

Showkat A. D., Anwar, H. & Peer, B. A., & Mansour, L. (2022). On Poisson Weighted Pranav Distribution Applicable to Count Data. An International Journal of Statistics Applications & Probability. 11(2). Pp. 385-393. http://dx.doi.org/10.18576/jsap/110202.

Yupapin A. (2023). A New Poisson Generalized Lindley Regression Model. Austrian Journal of Statistics. Volume 52, 39-50. http://www.ajs.or.at/doi:10.17713/ajs.v52i1.1344.

Zamani, H, Faroughi, P. & Ismail, N. (2015). Bivariate Poisson-Lindley Distribution with Application. Journal of Mathematics and Statistics. 11(1): pp.1-6. doi: 10.3844/jmssp.2015.1.6

Zamani, H., Ismail, N. & Shekari, M. (2018). Weighted Negative Binomial-Poisson Lindley with Application to Genetic Data. J Biostat Epidemiol. 4(3): pp. 136-141.