Kumaraswamy-Janardan Distribution: A Generalized Janardan Distribution with Application to Real Data

Abstract

The quest to improve on flexibility of probability distributions motivated this research. Four-parameter Janardan generalized distribution known as Kumaraswamy-Janardan distribution is proposed through method of parameterization and studied. The probability density function, cumulative density function, survival rate function as well as hazard rate function of the distribution are established. Statistical properties such as moments, moment generating function as well as maximum likelihood of the model are discussed. The parameters are estimated using the simulated annealing optimization algorithm. Flexibility of the model in comparison with the baseline model as well as other competing sub-models is verified using Akaike Information Criteria (AIC). The model is tested with real data and is proven to be more flexible in fitting real data than any of its sub-models considered.

Odd Chen Exponential Distribution: Properties and Applications

“…Ranneby [15] proposed the maximum product spacing (MPS) method as an alternative to MLE for estimating the unknown parameters of continuous univariate distributions [8] and independently developed the MPS method as an approximation to the Kullback-Leibler measure of information. Cheng [16] demonstrated that this method is as efficient as MLE estimators and consistent under more general conditions, which motivated our choice.…”

“…As a result, several studies have developed a more flexible distribution. See [3][4][5][6][7][8][9].…”