Accommodation of outliers by robust MML estimation for spatial autoregressive model

Some outliers might be undetected even after using the outlier detection procedures. In this paper, an accommodation procedure for such undetected outliers is discussed, which is done by robust modified maximum likelihood (MML) estimation of Type II censored sample for a Spatial Autoregressive (SAR) model. A new method is proposed for determining the number of observation to be censored to obtain robust parameter estimates. Next, a Monte-Carlo simulation study is carried to assess the robustness of the obtained MML estimators for both the normal and the contaminated normal models. Also, asymptotic variances, covariances, and distributional results are obtained for the MML estimators.