Parameter estimation procedures for log exponential-power distribution with real data applications

Abstract

In this study, some estimation techniques were investigated to estimate of parameters of the log exponential-power distribution. Maximum likelihood, quantile, least squares, weighted least squares, Anderson-Darling, and Cramer-von Mises estimation methods are studied in detail. The efficiency of these estimators was validated through Monte Carlo simulation experiments. To assess the performance of these estimators, Monte Carlo simulation studies were conducted out. Also, four real data applications are performed and Kolmogorov-Smirnov statistic results for all estimators are presented.