Parameter estimation methods for the Weibull‐Pareto distribution. Issue 1 (9th August 2019)

• Record Type: Journal Article
• Authors: Dey, Sanku; Alzaatreh, Ayman; Ghosh, Indranil
• Other Names: Marques Filipe guestEditor.; Ghosh Indranil guestEditor.; Mallick Avishek guestEditor.; Mota Pedro guestEditor.
• Is Part Of: Computational and mathematical methods. Volume 3:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-09
• Subjects: hazard rate function -- order statistics -- parameter estimation -- Weibull‐Pareto distribution; Mathematics -- Data processing -- Periodicals; Numerical analysis -- Periodicals; Numerical analysis; Mathematics -- Data processing; Periodicals; 004.0151
• DOI: 10.1002/cmm4.1053
• Languages: English
• ISSNs: 2577-7408

Abstract

Abstract : Recently there has been a growing interest to study various estimation procedures to estimate the model parameters of the Weibull Pareto distribution (WPD). It is observed that the proposed distribution can be used quite effectively to model skewed data. They also proposed a modification of the maximum likelihood method (MML) to estimate the parameters of the WPD. The proposed methods showed a better performance from the standard maximum likelihood (ML) method. However, when the shape parameter c >>1, the modified ML method showed unforgettable large bias and standard deviation. In this article, we address some new properties and propose different methods of estimation of the unknown parameters of a WPD from the frequentist point of view. We briefly describe different frequentist approaches, namely, moments estimators, L‐moments estimators, percentile‐based estimators, least squares estimators, method of maximum product of spacings estimators, method of Cramér‐von‐Mises estimators, method of Anderson‐Darling estimators. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and compare them with modified maximum likelihood estimation from the work of Alzaatreh et al (2013a). A real data set have been analyzed for illustrative purposes. Finally, bootstrap confidence intervals are obtained for the parameters of the model based on a real data set.