Sibling Regression for Generalized Linear Models

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