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Biases in the estimation of transfer function prediction errors

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Abstract

[1] In the quest for more precise sea-surface temperature reconstructions from microfossil assemblages, large modern training sets and new transfer function methods have been developed. Realistic estimates of the predictive power of a transfer function can only be calculated from an independent test set. If the test set is not fully independent, the error estimate will be artificially low. We show that the modern analogue technique using a similarity index (SIMMAX) and the revised analogue method (RAM), both derived from the modern analogue technique, achieve apparently lower root mean square error of prediction (RMSEP) by failing to ensure statistical independence of samples during cross validation. We also show that when cross validation is used to select the best artificial neural network or modern analogue model, the RMSEP based on cross validation is lower than that for a fully independent test set.

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