General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models (original) (raw)

High breakdown-point regression estimators protect against large errors and data contamination. Motivated by some -the least trimmed squares and maximum trimmed likelihood estimators -we propose a general trimmed estimator, which unifies and extends many existing robust procedures. We derive here the consistency and rate of convergence of the proposed general trimmed estimator under mild β-mixing conditions and demonstrate its applicability in nonlinear regression, time series, limited dependent variable models, and panel data.

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