  Cross validation residuals are well known for the ordinary least squares
model. Here leave-M-out cross validation is extended to generalised least
squares. The relationship between cross validation residuals and Cook's
distance is demonstrated, in terms of an approximation to the difference in the
generalised residual sum of squares for a model fit to all the data (training
and test) and a model fit to a reduced dataset (training data only). For
generalised least squares, as for ordinary least squares, there is no need to
refit the model to reduced size datasets as all the values for K fold cross
validation are available after fitting the model to all the data.
