High breakdown-point regression estimators protect against large errors and data con- tamination. We generalize the concept of trimming used by many of these robust estima- tors, 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.
|Place of Publication||Tilburg|
|Number of pages||41|
|Publication status||Published - 2007|
|Name||CentER Discussion Paper|
- asymptotic normality
- robust estimation