Detecting Errors in a Numerical Response via any Regression Model

arXiv:2305.16583v3 Announce Type: replace-cross
Abstract: Noise plagues many numerical datasets, where the recorded values in the data may fail to match the true underlying values due to reasons including: erroneous sensors, data entry/processing mistakes, or imperfect human estimates. We consider general regression settings with covariates and a potentially corrupted response whose observed values may contain errors. By accounting for various uncertainties, we introduced veracity scores that distinguish between genuine errors and natural data fluctuations, conditioned on the available covariate information …

abstract arxiv cs.lg data data entry datasets errors general human match mistakes noise numerical processing regression sensors stat.ml true type values via