Abstract
In this paper, we consider one-parameter regularly varying discrete distribution generated by Levy probability (RDLP). Some useful plots of the models are illustrated. Mathematically, to propose the RDLP model as a discrete distribution in bioinformatics, some common statistical facts such as unimodality, skewness to the right, upward/downward convexity, regular variation at infinity, and asymptotically constant slowly varying component are established for the model. Based on the Monte Carlo method and limited-memory Broyden–Fletcher–Goldfarb–Shanno for bound-constrained optimization, simulation studies are done to estimate bias, mean square error, and maximum likelihood estimation for the unknown parameter of the model. Some asymptotic properties and asymptotic expansions are given for useful functions. Moreover, we shall attempt to examine the RDLP model with a real count data set to show its application, and also compare it with two rival models.
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The authors confirm that the data supporting the findings of this study are available within the article.
Code Availability
Simulation studies, drawing figures and fitting the models have been done with the help of R statistical software. The R codes are available from the corresponding author upon request.
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The authors would like to thank the Editor-in-Chief, Associate Editor and the three anonymous referees for their valuable and constructive suggestions which led to significant improvements of the quality of our paper.
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Farbod, D., Basirat, M. ON THE DISCRETE DISTRIBUTION GENERATED BY LEVY PROBABILITY. J Math Sci 280, 198–211 (2024). https://doi.org/10.1007/s10958-023-06808-0
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DOI: https://doi.org/10.1007/s10958-023-06808-0