Sibling Regression for Generalized Linear Models
This work addresses bias in error correction for ecological and social science data, but it is incremental as it builds on existing non-parametric techniques.
The paper tackled systematic measurement errors in field observations by developing a residual-based approach for generalized linear models, demonstrating its effectiveness on synthetic data and moth surveys.
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.