GENERAL TRIMMED ESTIMATION: ROBUST APPROACH TO NONLINEAR AND LIMITED DEPENDENT VARIABLE MODELS

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+