Niyoyunguruza, AuriseOdongo, Leo OdiwuorNyarige, EunaHabineza, AlexisMuse, Abdisalam Hassan2023-07-252023-07-252023Niyoyunguruza, A., Odongo, L.O., Nyarige, E., Habineza, A. and Muse, A.H. (2023) Marshall-Olkin Exponentiated Fréchet Distribution. Journal of Data Analysis and Information Processing, 11, 262-292. https://doi.org/10.4236/jdaip.2023.113014https://doi.org/10.4236/jdaip.2023.113014http://ir-library.ku.ac.ke/handle/123456789/26404articleIn this paper, a new distribution called Marshall-Olkin Exponentiated Fréchet distribution (MOEFr) is proposed. The goal is to increase the flexibility of the existing Exponentiated Fréchet distribution by including an extra shape parameter, resulting into a more flexible distribution that can provide a better fit to various data sets than the baseline distribution. A generator method introduced by Marshall and Olkin is used to develop the new distribution. Some properties of the new distribution such as hazard rate function, survival function, reversed hazard rate function, cumulative hazard function, odds function, quantile function, moments and order statistics are derived. The maximum likelihood estimation is used to estimate the model parameters. Monte Carlo simulation is used to evaluate the behavior of the estimators through the average bias and root mean squared error. The new distribution is fitted and compared with some existing distributions such as the Exponentiated Fréchet (EFr), Marshall-Olkin Fréchet (MOFr), Beta Exponential Fréchet (BEFr), Beta Fréchet (BFr) and Fréchet (Fr) distributions, on three data sets, namely Bladder cancer, Carbone and Wheaton River data sets. Based on the goodness-of-fit statistics and information criteria values, it is demonstrated that the new distribution provides a better fit for the three data sets than the other distributions considered in the study.enExponentiated Fréchet DistributionMaximum Likelihood EstimationMarshall-Olkin FamilyArticle