A Probabilistic Model for Identifying Errors in Data Editing

Abstract Certain data screening systems incorporate large numbers of logical checks on data entering the system. When violated, these logical checks indicate that various combinations of variates are in error. This article provides a model for assigning a probability measure to identify variates in error when there is a simultaneous violation of a set of logical checks. For certain symmetry conditions, the measure is a reasonable approximation to the posterior probability that given a violation of a set of conditions, a variate is in error.