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Biometrika 1994 81(2):351-358; doi:10.1093/biomet/81.2.351
© 1994 by Biometrika Trust
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A cross-validatory method for dependent data

PRABIR BURMAN1, EDMOND CHOW2 and DEBORAH NOLAN2

1Division of Statistics, University of California Davis, California 95616, U. S.A
2Department of Statistics, University of California Berkeley, California 94720, U. S.A.

In this paper we extend the technique of cross-validation to the case where observations form a general stationary sequence. We call it h-block cross-validation, because the idea is to reduce the training set by removing the h observations preceding and following the observation in the test set. We propose taking h to be a fixed fraction of the sample 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 proposed modification over the cross-validation technique are demonstrated via simulation.

Key Words: Cross-validation • Dependence • Integrated square error • Prediction error


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