Combining Empirical Likelihood and Robust Estimation Methods for Linear Regression Models

TL;DR

This paper proposes a robust empirical likelihood approach for linear regression that improves parameter estimation in the presence of outliers and heavy-tailed errors, combining nonparametric and robust methods.

Contribution

It introduces a novel robust empirical likelihood method with constraints based on robust M estimation, enhancing performance with outliers.

Findings

Robust EL method outperforms traditional methods with outliers.

Simulation shows improved accuracy under heavy-tailed errors.

Real data example confirms effectiveness in practical scenarios.

Abstract

Ordinary least square (OLS), maximum likelihood (ML) and robust methods are the widely used methods to estimate the parameters of a linear regression model. It is well known that these methods perform well under some distributional assumptions on error terms. However, these distributional assumptions on the errors may not be appropriate for some data sets. In these case, nonparametric methods may be considered to carry on the regression analysis. Empirical likelihood (EL) method is one of these nonparametric methods. The EL method maximizes a function, which is multiplication of the unknown probabilities corresponding to each observation, under some constraints inherited from the normal equations in OLS estimation method. However, it is well known that the OLS method has poor performance when there are some outliers in the data. In this paper, we consider the EL method with robustifyed constraints. The robustification of the constraints is done by using the robust M estimation methods for regression. We provide a small simulation study and a real data example to demonstrate the capability of the robust EL method to handle unusual observations in the data. The simulation and real data results reveal that robust constraints are needed when heavy tailedness and/or outliers are possible in the data.