  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.
