An alternative algorithm of the empirical likelihood estimation for the parameter of a linear regression model

ABSTRACT

Empirical likelihood (EL) is a nonparametric method based on observations. EL method is defined as a constrained optimization problem. The solution of this constrained optimization problem is carried on using duality approach. In this study, we propose an alternative algorithm to solve this constrained optimization problem. The new algorithm is based on a newton-type algorithm for Lagrange multipliers for the constrained optimization problem. We provide a simulation study and a real data example to compare the performance of the proposed algorithm with the classical algorithm. Simulation and the real data results show that the performance of the proposed algorithm is comparable with the performance of the existing algorithm in terms of efficiencies and cpu-times.

KEYWORDS:

• Empirical likelihood
• lagrange multipliers
• linear regression

MATHEMATICS SUBJECT CLASSIFICATION:

• 62G08
• 65C60

Acknowledgments

The authors would like to thank an anonymous referee and the editor for their vary valuable comments, corrections and remarks which improve this work.