errorevaluated
Errorevaluated is a term used in statistics and data science to describe a post-estimation process in which an initial error estimate or predictive residual is re-examined and re-estimated using revised data, additional observations, or alternative modeling assumptions. The goal is to correct for potential misspecification, reduce bias, and improve the reliability of uncertainty quantification. While not a formally codified method in all disciplines, the concept is employed wherever practitioners seek to refine error assessments after an initial pass.
The typical errorevaluated workflow involves detecting signs of model misspecification or drift, selecting an alternative or
Applications of errorevaluated span multiple domains, including sensor fusion, meteorology, finance, and machine learning. In sensor
See also: recalibration, error analysis, model validation, online learning.