Abstract: Field observations form the basis of many scientific studies, especially in
ecological and social sciences. Despite efforts to conduct such surveys in a
standardized way, observations can be prone to systematic measurement errors.
The removal of systematic variability introduced by the observation process, if
possible, can greatly increase the value of this data. Existing non-parametric
techniques for correcting such errors assume linear additive noise models. This
leads to biased estimates when applied to generalized linear models (GLM). We
present an approach based on residual functions to address this limitation. We
then demonstrate its effectiveness on synthetic data and show it reduces
systematic detection variability in moth surveys.