A Novel Method for Developing Efficient Probability Distributions with Applications to Engineering and Life Science Data
Alamgir Khalil, Abdullah Ali H. Ahmadini, Muhammad Ali, Wali Khan Mashwani, Shokrya S. Alshqaq, Zabidin Salleh
Abstract
In this paper, a new approach for deriving continuous probability distributions is developed by incorporating an extra parameter to the existing distributions. Frechet distribution is used as a submodel for an illustration to have a new continuous probability model, termed as modified Frechet (MF) distribution. Several important statistical properties such as moments, order statistics, quantile function, stress-strength parameter, mean residual life function, and mode have been derived for the proposed distribution. In order to estimate the parameters of MF distribution, the maximum likelihood estimation (MLE) method is used. To evaluate the performance of the proposed model, two real datasets are considered. Simulation studies have been carried out to investigate the performance of the parameters’ estimates. The results based on the real datasets and simulation studies provide evidence of better performance of the suggested distribution.