A CROSS-VALIDATORY METHOD FOR DEPENDENT DATA

In this paper we extend the technique of cross-validation to the case where observations form a general stationary sequence. We call it h-bl ock cross-validation, because the idea is to reduce the training set b y removing the h observations preceding and following the observation in the test set. We propose taking h to be a fixed fraction of the sam ple size, and we add a term to our h-block cross-validated estimate to compensate for the underuse of the sample. The advantages of the prop osed modification over the cross-validation technique are demonstrated via simulation.