Derivation of Some Properties of New Topp Leone Exponential – Weibul Distribution and Its Application on Cancer Data Sets

Authors

A. A Sanusi ORCID icon for A. A Sanusi

Department of Mathematics and Statistics, Federal University of Kashere, Gombe State (Nigeria)

F. N Musa

Department of Mathematics and Statistics, Kaduna Polytechnic, Kaduna State (Nigeria)

A. U Shelleng

Department of Statistics, Gombe State University, Gombe State (Nigeria)

Article Information

DOI: 10.51584/IJRIAS.2026.11070194

Subject Category: Mathematics

Volume/Issue: 11/7 | Page No: 2695-2714

Publication Timeline

Submitted: 2026-08-03

Accepted: 2026-08-08

Published: 2026-08-20

Abstract

The obtainable breast and bladder cancer data sets are asymmetric and skewed in nature with heavy tail and variable hazard function; therefore, a new Topp Leone Exponential – Weibul (TLE-W) distribution with four distinct parameters was developed to fit these asymmetric data sets. Thus, this new distribution was developed by incorporating existing Weibul distribution into Topp Leone Exponential G family of distributions. More so, the respective density and distribution functions of this new Topp Leone Exponential – Weibul distribution were derived alongside with some respective mathematical properties such as moments, quantile function, renyi entropy and order statistics. Also, maximum likelihood estimation and maximum product of spacing methods were used to estimate the distinct four parameters of TLE – W. However, in the simulation study conducted; the results show that the four estimated parameters of Topp Leone Exponential – Weibul distribution are consistent as the bias and root mean square error approach zero. Conclusively, the breast and bladder cancer survival time data sets were used to validate the results obtained from MLE methods on this new distribution. The results show that Topp Leone Exponential – Weibul distribution best fit the two cancer data sets compare to the competitive distributions used in this study. Perhaps, this new distribution would be useful to model positive real life time data sets that are asymmetrical in nature with heavy tails and as well handle data’s volatility during instability.

Keywords

Topp Leone Exponential – Weibul distribution; Mathematical properties; Maximum Likelihood Estimation; Simulation study; Application to breast and bladder cancer data sets.

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References

1. Alzaatreh A, Lee C. and Famoye F. (2013). A New method for generating families of Distributions. Metron, 71: 63 - 79. [Google Scholar] [Crossref]

2. Abouelmagd. T. H. M., Mohammed S. H. and Haitham M. Y. (2019). Poisson Burr X Weibull distribution Journal of Nonlinear Science and Application. 12, pp 173–183. [Google Scholar] [Crossref]

3. Ahmad, Z. and Iqbal, B. (2017). Generalized flexible weibull extension distribution. Circulation in Computer, 2(4):68 – 75. [Google Scholar] [Crossref]

4. Al-Babtain, A., Fattah, A. A., Ahmed, A. H. N., and Merovci, F. (2017). The kumaraswamy – transmuted exponentiated modified weibull distribution. Communications in Statistics-Simulation and Computation. 46(5):3812{3832. [Google Scholar] [Crossref]

5. Almalki, S. J. (2018). A reduced new modified weibull distribution. Communications in Statistics-Theory and Methods, 47(10):2297 – 2313. [Google Scholar] [Crossref]

6. Aryal, G. R., Ortega, E. M., Hamedani, G., and Yousof, H. M. (2016). The Topp – leone generated weibull distribution: regression model, characterizations and applications. International Journal of Statistics and Probability, 6(1):126. [Google Scholar] [Crossref]

7. Bebbington, M., Lai, C. D., and Zitikis, R. (2007). A flexible weibull extension. Reliability Engineering & System Safety, 92(6):719 – 726. [Google Scholar] [Crossref]

8. Cordeiro, G. M., Ortega, E. M., and Nadarajah, S. (2010). The kumaraswamy weibull distribution with application to failure data. Journal of the Franklin Institute, 347(8):1399 – 1429 [Google Scholar] [Crossref]

9. Diamond, O. T., Festus, C. O., and Ekhosuehi,, N. (2021). The topp leone weibull distribution: its properties and application. Earth line Journal of Mathematical Sciences, 7(2): 381 - 401. [https://doi.org/10.341981/ejms.7221.381401]. [Google Scholar] [Crossref]

10. Gauss M. C., Edwin M.M.O., and Artur J. L. (2013). The exponential–weibull lifetime distribution. Journal of Statistical Computation and Simulation. DOI:10.1080/00949655.2013.797982. [Google Scholar] [Crossref]

11. Ghitany, M., Al-Hussaini, E., and Al-Jarallah, R. (2005). Marshall olkin extended weibull distribution and its application to censored data. Journal of Applied Statistics, 32(10):1025 – 1034. [Google Scholar] [Crossref]

12. Ivana, P., Zuzana, S., and Maria, M. (2018): Transmuted weibull distribution and its application. MATEC web conference 157, 08007. [https: //doi.org/10.1051/matecconf/201815708007]. [Google Scholar] [Crossref]

13. Jiang, R.; Murthy, D.N.P. (2011). "A study of Weibull shape parameter: Properties and significance". Reliability Engineering & System Safety. 96 (12): 1619–26. doi:10.1016/j.ress.2011.09.003 [Google Scholar] [Crossref]

14. Lai, C.-D. (2014). Generalized weibull distributions. In Generalized weibull distributions, pages 23 – 75. Springer. [Google Scholar] [Crossref]

15. Lee, C., Famoye, F., and Olumolade, O. (2007). Beta-weibull distribution: some properties and applications to censored data. Journal of modern applied statistical methods, 6(1):17. [Google Scholar] [Crossref]

16. Lee, E. T. and Wang, J. W: Statistical methods for survival data analysis (3rd Edition), John Wiley and Sons, New York, USA, 535 Pages, (2003) ISBN 0-471-36997-7. [Google Scholar] [Crossref]

17. Lee, E. T. (1992). Statistical methods for survival data analysis (2nd Edition), John Wiley and Sons Inc., New York, USA, 156 Pages. [Google Scholar] [Crossref]

18. Noor A. I. and Mundher A. K. (2018). Exponentiated kumaraswamy exponentiated weibull distribution With Application. Jurnal Karya Asli Lorekan Ahli Matematik. 11(1): 015-022. [Google Scholar] [Crossref]

19. Oguntunde, P. E., Odetunmibi, O. A., and Adejumo., A. O. (2015). On the Exponentiated generalized weibull distribution: A Generalization of the Weibull distribution. Indian Journal of Science and Technology. 8(35). [Google Scholar] [Crossref]

20. Nofal, Z. M., A_fy, A. Z., Yousof, H. M., Granzotto, D. C. T., and Louzada, F. (2018). The transmuted exponentiated additive weibull distribution: properties and applications. Journal of Modern Applied Statistical Methods,17(1):4. [Google Scholar] [Crossref]

21. Pal. M., Ali. M.M., Woo J. (2006). Exponentiated Weibull Distribution. STATISTICA, anno LXVI, n. 2, 2006 [Google Scholar] [Crossref]

22. Rayleigh Distribution – MATLAB & Simulink – Math Works Australia. www.mathworks.com.au. [Google Scholar] [Crossref]

23. Ramos, M. A., Cordeiro, G. M., Marinho, P. D., Dias, C. B. and Hamadani, G. G. (2013). The zografos-balakrishman log-logistic distribution: properties and applications, Journal of Statistical Theory and Applications, 12(3): 225-244. [Google Scholar] [Crossref]

24. Sanusi, A. A, Doguwa, S.I.S, Audu, I and Baraya, Y. M. (2020). Topp Leone exponential – generalized inverted exponential distributions: Properties and Applications. Communication in Physical Sciences, 8(4): 442-455, 2022. [Google Scholar] [Crossref]

25. Salman, A., Gamze. O., Saman. H. S., and Muhammad Q. S. (2019) A New Generalized Weighted Weibull Distribution Pakistan Journal of Statistics and Operation Research . 15(1) pp161-178 DOI: 10.18187/pjsor.v15i1.2782 [Google Scholar] [Crossref]

26. Zhang, T. and Xie, M. (2011). On the upper truncated weibull distribution and its reliability implications. Reliability Engineering & System Safety, 96(1):194 – 200. [Google Scholar] [Crossref]

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