Robust calibration

This article presents robust methods for the random calibration problem. Many calibration techniques are based on regression models or measurement-error models. Prediction from such models is known to be highly nonrobust, and robust techniques should prove quite valuable. Robustcalibration procedures are procedures that work well even if there is some contamination in the data or if the model assumptions used in deriving the procedure are not quite true for the given data. Several approaches to robustifying calibration are compared theoretically, by Monte Carlo simulation, and on real data.

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