Conditional Maximum Lq-Likelihood Estimation for Regression Model with Autoregressive Error Terms
Statistics Theory
2018-04-23 v1 Computation
Statistics Theory
Abstract
In this article, we consider the parameter estimation of regression model with pth order autoregressive (AR(p)) error term. We use the Maximum Lq-likelihood (MLq) estimation method that is proposed by Ferrari and Yang (2010a), as a robust alternative to the classical maximum likelihood (ML) estimation method to handle the outliers in the data. After exploring the MLq estimators for the parameters of interest, we provide some asymptotic properties of the resulting MLq estimators. We give a simulation study and a real data example to illustrate the performance of the new estimators over the ML estimators and observe that the MLq estimators have superiority over the ML estimators when outliers are present in the data.
Cite
@article{arxiv.1804.07600,
title = {Conditional Maximum Lq-Likelihood Estimation for Regression Model with Autoregressive Error Terms},
author = {Yesim Guney and Yetkin Tuac and Senay Ozdemir and Olcay Arslan},
journal= {arXiv preprint arXiv:1804.07600},
year = {2018}
}