Robust statistics

Summary

Robust statistics studies statistical procedures whose results change little when the data depart slightly from the model assumptions. Such departures include outliers or a heavier tail than assumed. Two main measures of robustness are the breakdown point and the influence function. The first says how much bad data an estimator can absorb, and the second how much a single point can move it. Typical robust procedures are the median, trimmed means and other L-estimators, and Huber's M-estimators. The field was shaped by Tukey, Huber and Hampel from the 1960s on.