Extended Odd Lomax Family of Distributions: Properties and Applications


  • Abdul Ghaniyyu Abubakari C.K. Tedam University of Technology and Applied Sciences
  • Claudio Chadli Kandza-Tadi Marien Ngouabi University
  • Ridwan Rufai Dimmua University of Ghana




Odd Lomax distribution, Family of distributions, Quantile function


The Lomax distribution has a wide range of applications. Due to this, it has had many extensions to render it more flexible and useful to model real world data. In this study, a new family of distributions called the extended odd Lomax family of distributions is introduced by adding two extra shape parameters and one scale parameter. We derived several statistical properties of the new family of distributions. The parameters of the family of distributions are estimated by the use of maximum likelihood method and the consistency of the estimators investigated via Monte Carlo simulations. The usefulness and flexibility of the new family of distributions are illustrated by the use of two real datasets. The results show that the distributions adequately describe the datasets.


A. AFIFY, G. CORDEIRO, H. M. YOUSOF, A. ALZAATREH, Z. NOFAL (2016). The Kumaraswamy transmuted-G family of distributions: Properties and applications. Journal of Data Science, 14, no. 2, pp. 245–270.

A. Z. AFIFY, Z. M.NOFAL, H. M. YOUSOF, Y. M. E.GEBALY, N. S. BUTT (2015). The transmuted Weibull Lomax distribution: Properties and application. Pakistan Journal of Statistics and Operation Research, 11, no. 1, pp. 135–153.

A. ALZAATREH, C. LEE, F. FAMOYE (2013). A new method for generating families of continuous distributions. Metron, 71, no. 1, pp. 63–79.

Z. BIRNBAUM, S. SAUNDERS (1969). Estimation for a family of life distributions with applications to fatigue. Journal of Applied Probability, 6, no. 2, pp. 328–347.

K. COORAY (2006). Generalization of the Weibull distribution: The odd Weibull family. Statistical Modelling: An International Journal, 6, no. 3, pp. 265–277.

G. CORDEIRO, A. AFIFY, E. ORTEGA, A. SUZUKI, M. E. MEAD (2019). The odd Lomax generator of distributions: Properties, estimation and applications. Journal of Computational and Applied Mathematics, 347, pp. 222–237.

M. A. HAQ, M. ELGARHY (2018). The odd Fréchet-G family of probability distributions. Journal of Statistics Applications & Probability, 7, no. 1, pp. 189–203.

M. E. MEAD (2016). On five-parameter Lomax distribution: Properties and applications. Pakistan Journal of Statistics and Operation Research, 12, no. 1, pp. 185–199.

S.NASIRU (2018). Extended odd Fréchet-G family of distributions. Journal of Probability and Statistics, 2018, pp. 1–12.

P. E. OGUNTUNDE, M. A. KHALEEL, M. T. AHMED, A. O. ADEJUMO, O. A. ODETUNMIBI (2017). A new generalization of the Lomax distribution with increasing, decreasing, and constant failure rate. Modelling and Simulation in Engineering, 2017,

pp. 1–6.

K. XU, M. XIE, L. TANG, S. HO (2003). Application of neural networks in forecasting engine systems reliability. Applied Soft Computing, 2, no. 4, pp. 255–268.

H. M. YOUSOF, A. Z. AFIFY, G. G. HAMEDANI, G. ARYAL (2017). The Burr X generator of distributions for lifetime data. Journal of Statistical Theory and Applications, 16, no. 3, p. 288.




How to Cite

Abubakari, A. G., Kandza-Tadi, C. C., & Dimmua, R. R. (2020). Extended Odd Lomax Family of Distributions: Properties and Applications. Statistica, 80(3), 331–354. https://doi.org/10.6092/issn.1973-2201/9765