Abstract
The present article introduces a new distribution called the modified Kies-Frechet (MKF) distribution that extends the Frechet distribution and provides two new sub-models called modified Kies inverse-exponential and modified Kies inverse-Rayleigh distributions. The MKF model can provide left-skewed, symmetrical, right-skewed, J-shaped, and reversed-J shaped densities. The MKF density was expressed as a linear combination of Frechet densities. We derive some basic mathematical properties of the MKF model. The MKF parameters are estimated using some classical estimation methods called, the maximum likelihood, Anderson-Darling, least-squares, Cramer-von Mises, and weighted least squares. The performances of these estimators were explored by a detailed simulation study. Finally, the flexibility of the MKF model is checked using a real data set, showing that it can provide close fit as compared with other competing models.