High breakdown-point regression estimators protect against large errors and data con- tamination. Motivated by some { the least trimmed squares and maximum trimmed like- lihood estimators { we propose a general trimmed estimator, which uni-es and extends many existing robust procedures. We derive here the consistency and asymptotic distri- bution of the proposed general trimmed estimator under mild --mixing conditions and demonstrate its applicability in nonlinear regression, time series, and limited dependent variable models.
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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number
2007-1.
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Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models
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