In recent years so many discrete and continuous distributions are derived in the literature using the technique of different family of distributions to generalize or extend. The aim of this article is to propose a new generalization of exponential-exponential distribution by the method of Kumaraswamy G (Kw-G) family of distributions. The new proposed distribution is a three-parameter distribution and is termed as Kumaraswamy Exponential-Exponential (Kw EED) distribution and derived its probability and survival functions to study the behavior of the plot by various values of parameter. We discussed the properties, incomplete moments, Shannon and Renyi entropy and order statistics. The Lorenz and Bonferroni curve functions are derived with the help of quantile function. Maximum likelihood estimation is used for the estimation of the model parameter. The stochastic ordering of the probability distribution is also included in this work. A simulation study is used to find the parameter values. The superiority and adaptability of the suggested model is described by comparing it with other models using some model criterions with the help of cancer data. The findings reveals that the proposed model is better fit than other compared models.
| Published in | International Journal of Statistical Distributions and Applications (Volume 12, Issue 1) |
| DOI | 10.11648/j.ijsda.20261201.11 |
| Page(s) | 1-12 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Kw-G Family of Distributions, Exponential-Exponential Distribution, Survival Function, Order Statistic, Maximum Likelihood Estimation
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APA Style
Jayalekshmi, L., Vijayakumar, M., Santhamani, S. D. (2026). A New Generalization of Exponential-Exponential Distribution Using Kumaraswamy-G Family of Distribution: Theory and Application to Cancer Data. International Journal of Statistical Distributions and Applications, 12(1), 1-12. https://doi.org/10.11648/j.ijsda.20261201.11
ACS Style
Jayalekshmi, L.; Vijayakumar, M.; Santhamani, S. D. A New Generalization of Exponential-Exponential Distribution Using Kumaraswamy-G Family of Distribution: Theory and Application to Cancer Data. Int. J. Stat. Distrib. Appl. 2026, 12(1), 1-12. doi: 10.11648/j.ijsda.20261201.11
AMA Style
Jayalekshmi L, Vijayakumar M, Santhamani SD. A New Generalization of Exponential-Exponential Distribution Using Kumaraswamy-G Family of Distribution: Theory and Application to Cancer Data. Int J Stat Distrib Appl. 2026;12(1):1-12. doi: 10.11648/j.ijsda.20261201.11
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Download@article{10.11648/j.ijsda.20261201.11,
author = {Latha Jayalekshmi and Mani Vijayakumar and Shibu Damodaran Santhamani},
title = {A New Generalization of Exponential-Exponential Distribution Using Kumaraswamy-G Family of Distribution: Theory and Application to Cancer Data},
journal = {International Journal of Statistical Distributions and Applications},
volume = {12},
number = {1},
pages = {1-12},
doi = {10.11648/j.ijsda.20261201.11},
url = {https://doi.org/10.11648/j.ijsda.20261201.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsda.20261201.11},
abstract = {In recent years so many discrete and continuous distributions are derived in the literature using the technique of different family of distributions to generalize or extend. The aim of this article is to propose a new generalization of exponential-exponential distribution by the method of Kumaraswamy G (Kw-G) family of distributions. The new proposed distribution is a three-parameter distribution and is termed as Kumaraswamy Exponential-Exponential (Kw EED) distribution and derived its probability and survival functions to study the behavior of the plot by various values of parameter. We discussed the properties, incomplete moments, Shannon and Renyi entropy and order statistics. The Lorenz and Bonferroni curve functions are derived with the help of quantile function. Maximum likelihood estimation is used for the estimation of the model parameter. The stochastic ordering of the probability distribution is also included in this work. A simulation study is used to find the parameter values. The superiority and adaptability of the suggested model is described by comparing it with other models using some model criterions with the help of cancer data. The findings reveals that the proposed model is better fit than other compared models.},
year = {2026}
}
TY - JOUR T1 - A New Generalization of Exponential-Exponential Distribution Using Kumaraswamy-G Family of Distribution: Theory and Application to Cancer Data AU - Latha Jayalekshmi AU - Mani Vijayakumar AU - Shibu Damodaran Santhamani Y1 - 2026/01/19 PY - 2026 N1 - https://doi.org/10.11648/j.ijsda.20261201.11 DO - 10.11648/j.ijsda.20261201.11 T2 - International Journal of Statistical Distributions and Applications JF - International Journal of Statistical Distributions and Applications JO - International Journal of Statistical Distributions and Applications SP - 1 EP - 12 PB - Science Publishing Group SN - 2472-3509 UR - https://doi.org/10.11648/j.ijsda.20261201.11 AB - In recent years so many discrete and continuous distributions are derived in the literature using the technique of different family of distributions to generalize or extend. The aim of this article is to propose a new generalization of exponential-exponential distribution by the method of Kumaraswamy G (Kw-G) family of distributions. The new proposed distribution is a three-parameter distribution and is termed as Kumaraswamy Exponential-Exponential (Kw EED) distribution and derived its probability and survival functions to study the behavior of the plot by various values of parameter. We discussed the properties, incomplete moments, Shannon and Renyi entropy and order statistics. The Lorenz and Bonferroni curve functions are derived with the help of quantile function. Maximum likelihood estimation is used for the estimation of the model parameter. The stochastic ordering of the probability distribution is also included in this work. A simulation study is used to find the parameter values. The superiority and adaptability of the suggested model is described by comparing it with other models using some model criterions with the help of cancer data. The findings reveals that the proposed model is better fit than other compared models. VL - 12 IS - 1 ER -
Department of Statistics, Annamalai University, Annamalai Nagar, India
Department of Statistics, Annamalai University, Annamalai Nagar, India
Department of Statistics, University of Kerala, Thiruvananthapuram, India
