Derivation of The Generalized Gamma DistributionVia An Ordinary Differential Equation Approach: Theory and Applications
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https://doi.org/10.14419/3b25xq49
Received date: August 6, 2025
Accepted date: November 3, 2025
Published date: November 8, 2025
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Characterization; Differential Equation; Generalized Gamma Distribution; Truncated Moment -
Abstract
This paper introduces a novel approach for generating the Generalized Generalized Gamma (GGG) distribution by employing a second-order non-homogeneous non-linear differential equation with constant coefficients. The proposed framework yields a flexible univariate continuous probability distribution with a rich structure. We provide a comprehensive analysis of the distribution’s formulation, key statistical properties, and characterizations via truncated moments. Parameter estimation is carried out through both the Maximum Likelihood Estimation (MLE) and the Method of Moments. A simulation study is conducted to assess the performance of these estimators. The practical applicability of the GGG distribution is demonstrated using real-life datasets, with model adequacy evaluated through multiple goodness-of-fit criteria, including the Anderson-Darling and Cramér–von Mises tests. The empirical results highlight the superior performance of the proposed distribution in modeling complex data patterns. Findings and implications are thoroughly discussed, underscoring the GGG distribution’s potential in statistical modeling and applied probability.
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References
- M. Ahsanullah, Characterizations of Univariate Continuous Distributions, Atlantis-Press, Paris, France, 2017. https://doi.org/10.2991/978-94-6239-139-0
- H. Akaike, Information Theory as an Extension of the Maximum Likelihood Principle. In: Petrov, B.N., and Csaki, F., Eds., Second International Symposium on Information Theory, Akademiai Kiado, Budapest, 267-281 (1973).
- E. M. Almetwally, H. Z. Muhammed, and E. S. A. El-Sherpieny, Bivariate Weibull distribution: properties and different methods of estima-tion. Annals of Data Science, 7(1), 163-193 (2020). https://doi.org/10.1007/s40745-019-00197-5
- J. Galambos, and S. Kotz, Characterizations of probability distributions: A unified approach with an emphasis on exponential and related 18 models, Lecture Notes in Mathematics, 675, Springer, Berlin, Germany, 1978.
- W. Glanzel, A. Telcs, and A. Schubert, Characterization by truncated moments and its application to Pearson-type distributions, Z. Wahrsch. Verw. Gebiete, 66, 173 – 183 (1984). https://doi.org/10.1007/BF00531527
- S. Gradshteyn, and I. M. Ryzhik, Table of integrals, series, and products, Academic Press, Inc., San Diego, California, USA, 2000.
- E.T. Jaynes, Information theory and statistical mechanics. Phys. Rev. 106(4), 620–630 (1957). https://doi.org/10.1103/PhysRev.106.620
- E. Koudou, and C. Ley, Characterizations of GIG laws: A survey, Probability surveys, 11: 161 – 176 (2014). https://doi.org/10.1214/13-PS227.
- P. Langevin, Sur la théorie du mouvement brownien [On the Theory of Brownian Motion]. C. R. Acad. Sci. Paris. 146: 530–533 (1908).
- C. A. McGilchrist and C. W. Aisbett, Regression with frailty in survival analysis. Biometrics 47: 461–466 (1991). https://doi.org/10.2307/2532138.
- H. Nagaraja, Characterizations of Probability Distributions. In Springer Handbook of Engineering Statistics (pp. 79 - 95), Springer, London, UK, 2006. https://doi.org/10.1007/978-1-84628-288-1_4.
- K. Pearson, Contributions to the mathematical theory of evolution, II: Skew variation in homogeneous material, Philo- sophical Transactions of the Royal Society of London, A186 (1895), 343-414. https://doi.org/10.1098/rsta.1895.0010.
- K. Pearson, Mathematical contributions to the theory of evolution, X: Supplement to a memoir on skew of variation, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 197 (1901), 343-414. https://doi.org/10.1098/rsta.1901.0023.
- K. Pearson, Mathematical contributions to the theory of evolution, XIX: Second supplement to a memoir on skew of variation, Philosophical Trans-actions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 216 (1916), 429-457. https://doi.org/10.1098/rsta.1916.0009.
- V. D. Rao, S. V. S. Girija, and Y. Phani, Differential Approach to Cardioid Distribution, Computer Engineering and Intelligent Systems, Vol 2, No.8 (2011), 1-6.
- G. Schwarz, Estimating the dimension of a model. The annals of statistics, 6 (2), 461 – 464 (1978). https://doi.org/10.1214/aos/1176344136.
- M. Shakil, M. Ahsanullah, and B. M. G. Kibria, On the Characterizations of Chen’s Two-Parameter Exponential Power Life-Testing Distribution, Journal of Statistical Theory and Applications, 17(3) (2018), 393-407. https://doi.org/10.2991/jsta.2018.17.3.1.
- M. Shakil, M. Ahsanullah, and B. M. G. Kibria, Some characterizations and applications of a size-biased weighted distribution useful in lifetime modelling, Journal of Statistics Applications & Probability, 10(3) (2021), 607-624. https://doi.org/10.18576/jsap/100301.
- M. Shakil, J. N. Singh, L. Tomy, R. C. S. Chandel, T. Hussain, A. Khadim, M. Ahsanullah, and B. M. G. Kibria. A First Order Linear Differential Equation Approach of Generating Continuous Probability Distributions Review, Analysis and Characterizations. Jnanabha, Vol. 54(2) (2024), 233-250. https://doi.org/10.58250/jnanabha.2024.54223
- C. E. Shannon, A mathematical theory of communication. Bell System Tech. J., Vol. 27 (1948), 379 – 423; 623 – 656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x.
- V. P. Singh, Systems of frequency distributions for water and environmental engineering, Physica A: Statistical Mechanics and its Applications, 506 (2018), 50-74. https://doi.org/10.1016/j.physa.2018.03.038.
- V. P. Singh, and L. Zhang, Pearson System of Frequency Distributions, Systems of Frequency Distributions for Water and Environmental Engi-neering, Chapter 2 (2020), 11 – 39, Cambridge University Press, USA. https://doi.org/10.1017/9781108859530.003.
- R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability & Statistics for Engineers & Scientists, 9th Edition, Page 13, Prentice Hall, New York, USA (2012).
-
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