Biasbound
Biasbound is a term used in statistics and machine learning to denote a formal bound on the bias of an estimator or predictor under a defined class of data-generating processes or modeling assumptions. The bias refers to the difference between the expected value of the estimator and the true quantity being estimated. A biasbound provides a guarantee that this difference does not exceed a specified value, either deterministically or with high probability.
Mathematically, if θ_hat is an estimator of θ, a biasbound states that sup over a class C of
Derivation methods include concentration inequalities, asymptotic expansions, bias-variance decompositions, regularization-based controls, and bootstrap arguments.
Applications include estimator design, model auditing, and fairness and reliability assessments in predictive systems, especially under
Limitations include sometimes loose or unverifiable bounds, dependence on modeling assumptions, and the potential for bounds