Abstract :
High-breakdown-point regression estimators protect against large errors and data
contamination+ We generalize the concept of trimming used by many of these
robust estimators, such as the least trimmed squares and maximum trimmed likelihood,
and propose a general trimmed estimator, which renders robust estimators
applicable far beyond the standard ~non!linear regression models+ We derive here
the consistency and asymptotic distribution of the proposed general trimmed estimator
under mild b-mixing conditions and demonstrate its applicability in nonlinear
regression and limited dependent variable models+
