Biaskorrekciós

Biaskorrekciós (bias correction) is a term used to describe methods and procedures that reduce or remove systematic error in estimators, statistics, or model outputs. The aim is to bring the expected value of the estimate closer to the true parameter, especially in finite samples where bias is pronounced. The notion is widely used across statistics, econometrics, and climate science.

In statistics and econometrics, bias correction involves subtracting an estimate of the bias from the estimator, or adopting bias-reducing procedures. Analytic methods use mathematical expansions to approximate bias; resampling methods estimate bias from data. Common techniques include jackknife bias correction, bootstrap bias correction (and the bias-corrected and accelerated, BCa, bootstrap), and penalized likelihood methods such as Firth's correction for reducing small-sample bias in maximum likelihood estimators.

In climate science and environmental modeling, bias correction refers to post-processing model output to align with historical observations. Techniques include mean bias correction and quantile mapping; while they can improve present-day fit, they may alter climate-change signals if not applied carefully and often assume some form of stationarity.

Practical use requires balancing bias reduction against variance inflation, and recognizing that bias correction does not fix model misspecification; it should be accompanied by validation and sensitivity analysis.