Optimal B-Robust Estimation for the Parameters of Marshall-Olkin Extended Burr XII Distribution and Application for Modeling Data from Pharmacokinetics Study

TL;DR

This paper develops optimal B-robust estimators for the parameters of the flexible Marshall-Olkin Extended Burr XII distribution and demonstrates their effectiveness in modeling pharmacokinetics data, addressing robustness issues of traditional methods.

Contribution

It introduces a new robust estimation method for MOEBXII distribution parameters and applies it to pharmacokinetics data, improving modeling robustness against outliers.

Findings

Robust estimators outperform ML and LS in simulations.

The MOEBXII distribution effectively models pharmacokinetics data.

Simulation results confirm the robustness of the proposed estimators.

Abstract

Marshall-Olkin Extended Burr XII (MOEBXII) distribution family, which is a generalization of Burr XII distribution proposed by Al-Saiari et al. [1] , is a flexible distribution that can be used in many fields such as actuarial science, economics, life testing, reliability and failure time modeling. The parameters of the MOEBXII distribution are usually estimated by the maximum likelihood (ML) and least squares (LS) estimation methods. However, these estimators are not robust to the outliers which are often encountered in practice. There are two main purposes of this paper. The first one is to find the robust estimators for the parameters of the MOEBXII distribution. The second one is to use this distribution for modeling data from pharmacokinetics study. To obtain the robust estimators we use the optimal B robust estimator proposed by Hampel et al. [2]. We provide a simulation study to show the performance of the proposed estimators for the ML, LS and robust M estimators. We also give a real data example to illustrate the modeling capacity of the MOEBXII distribution for data from pharmacokinetics study.