M-estimator

A broad class of estimators defined by minimizing or maximizing a sum of a chosen objective (loss or score) function, formalized by Peter Huber. Maximum likelihood and least squares are special cases; non-quadratic loss functions yield robust estimators resistant to outliers and heavy-tailed errors.