Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as and . Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, (using robust and non-robust measures), and our proposed and plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust and robust were not very successful in detecting IOs. The suffered from the masking effect, and the robust suffered from swamping in general spatial models. Interestingly, the results showed that the proposed plot, followed by the plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.
[Influential Observations, High Leverage Points, and Outliers in Linear Regression]: Comment: Aspects of Diagnostic Regression Analysis
