Journal article

Reducing variability of crossvalidation for smoothing-parameter choice

Peter Hall, Andrew P Robinson



One of the attractions of crossvalidation, as a tool for smoothing-parameter choice, is its applicability to a wide variety of estimator types and contexts. However, its detractors comment adversely on the relatively high variance of crossvalidatory smoothing parameters, noting that this compromises the performance of the estimators in which those parameters are used. We show that the variability can be reduced simply, significantly and reliably by employing bootstrap aggregation or bagging. We establish that in theory, when bagging is implemented using an adaptively chosen resample size, the variability of crossvalidation can be reduced by an order of magnitude. However, it is arguably more..

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University of Melbourne Researchers


Funding Acknowledgements

The authors are grateful to an associate editor and a referee for helpful comments, and to Dr Aurore Delaigle for her helpful contributions to our C programming. The research was partially supported by the Australian Research Council and an award under the Merit Allocation Scheme on the National Facility of the Australian Partnership for Advanced Computing.