OSAGIE S. A.

Several probability models have been introduced in literature to model data sets arising from different real-world scenarios. Such applications are found in the field of engineering, biological sciences, finance, demography, actuarial sciences, agricultural sciences, etc. In this study, we develop a hybrid statistical model which is a composition of the inverse Burr and Topp-Leone distribution. We refer to this model as the inverse Burr Topp-Leone (IBTL) distribution. Basic statistical properties of the IBTL distribution are derived. The method of maximum likelihood estimation is employed to obtain the unknown parameters of the IBTL distribution. A Monte Carlo simulation study is carried out to investigate the asymptotic behavior of the maximum likelihood estimates of the parameters of the IBTL distribution. Finally, two real data sets comprising of the germination rate of oil palm seeds are adapted to illustrate the usefulness of the proposed IBTL distribution in real-life data fittings. For the purpose of model comparison, we considered some existing unit-interval distributions. Result obtained from the analysis revealed that the IBTL distribution outperformed the competitor distributions and thus, becomes a useful alternative to other existing unit-interval distributions in real-life data fittings.