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Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology

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  • Muhammad Aslam

    (Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia)

Abstract

This paper addresses the modification of the F-test for count data following the Poisson distribution. The F-test when the count data are expressed in intervals is considered in this paper. The proposed F-test is evaluated using real data from climatology. The comparative study showed the efficiency of the F-test for count data under neutrosophic statistics over the F-test for count data under classical statistics.

Suggested Citation

  • Muhammad Aslam, 2022. "Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology," Stats, MDPI, vol. 5(3), pages 1-11, August.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:3:p:45-783:d:886968
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    References listed on IDEAS

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    1. Arias-Rosales, Andrés & Osorio-Gómez, Gilberto, 2018. "Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy," Applied Energy, Elsevier, vol. 228(C), pages 980-998.
    2. Lo Brano, Valerio & Orioli, Aldo & Ciulla, Giuseppina & Culotta, Simona, 2011. "Quality of wind speed fitting distributions for the urban area of Palermo, Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1026-1039.
    3. Viertl, Reinhard, 2006. "Univariate statistical analysis with fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 133-147, November.
    4. Dankmar Böhning & Patarawan Sangnawakij, 2021. "Count outcome meta-analysis for comparing treatments by fusing mixed data sources: comparing interventions using across report information," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 75-85, March.
    5. P. Filzmoser & R. Viertl, 2004. "Testing hypotheses with fuzzy data: The fuzzy p-value," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(1), pages 21-29, February.
    6. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    7. Ali, Sajid & Lee, Sang-Moon & Jang, Choon-Man, 2018. "Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea," Renewable Energy, Elsevier, vol. 123(C), pages 652-663.
    8. Puig, Pedro & Weiß, Christian H., 2020. "Some goodness-of-fit tests for the Poisson distribution with applications in Biodosimetry," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
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