Articles | Volume 12, issue 5
https://doi.org/10.5194/cp-12-1215-2016
https://doi.org/10.5194/cp-12-1215-2016
Technical note
 | 
24 May 2016
Technical note |  | 24 May 2016

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

Mathias Trachsel and Richard J. Telford

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.

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Short summary
In spatially structured environments, conventional cross validation results in over-optimistic transfer function performance estimates. 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. We assess three methods for determining the optimal h using simulated data and published transfer functions. Some transfer functions perform notably worse when h-block cross validation is used.