Parameter estimation for 3-parameter generalized pareto distribution by the principle of maximum entropy (POME)

Abstract

The principle of maximum entropy (POME) is employed to derive a new method of parameter estimation for the 3-parameter generalized Pareto (GP) distribution. Monte Carlo simulated data are used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). The parameter estimates yielded by the POME are either superior or comparable for high skewness. © 1995 Taylor & Francis Group, LLC.