Methods of Estimation for the Three-Parameter Reflected Weibull Distribution

TL;DR

This paper introduces new estimation methods for the three-parameter Reflected Weibull distribution, demonstrating improved performance over traditional estimators through simulations and real data applications.

Contribution

It proposes a novel Location and Scale Parameters free maximum likelihood estimator that overcomes unbounded likelihood issues.

Findings

The new estimator outperforms traditional methods in bias and RMSE.

Simulation results confirm the estimator's superior accuracy.

Real data examples illustrate practical applicability.

Abstract

In this paper, we propose methods for the estimation of parameters for the three-parameter Reflected Weibull distribution. The Moment estimator , Maximum likelihood estimator and Location and Scale Parameters free maximum likelihood estimator. The Location and Scale Parameters free maximum likelihood estimator is based on a data transformation, which avoids the problem of unbounded likelihood estimator. Through Mont Carlo simulations, we further show that the Location and Scale Parameters free maximum likelihood estimator performs better than methods moment and maximum likelihood estimator in terms of bias and root mean squared error. Finally, two examples based on real data sets are presented to illustrate methods.