Global Influence Diagnostics in Gaussian Spatial Linear Model with Multiple Repetitions

Repeated measures of data have been widely analyzed in many fields, such as biology and medicine, but we can find a few works that study repeated measures in the geostatistical field. In this work, we present global diagnostics techniques for case deletion to assess the influence of observations on Gaussian spatial linear models with multiple replications. We present Cook's distance likelihood-based and Q-function-based with one-step approximation. Moreover, we propose to use the expected information matrix and the expectation of the second derivative of the Q-function as part of this measure. An application to real data illustrates the methodology developed. The results show that the methods are effective on the detection of influential observations.