Technical note: Estimating unbiased transfer-function performances in spatially structured environments

Abstract

Conventional cross validation schemes for assessing transfer-function performance assume that observations are independent. In spatially structured environments this assumption is violated, resulting in over-optimistic estimates of transfer-function performance. H-block cross validation, where all samples within h kilometres of the test samples are omitted, is a method for obtaining unbiased transfer-function performance estimates. In this study, we assess three methods for determining the optimal h. Using simulated data, we find that all three methods result in comparable values of h. Applying the three methods to published transfer functions, we find they yield similar values for h. Some transfer functions perform notably worse when h-block cross validation is used.