General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)

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Abstract

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.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages41
Volume2007-65
Publication statusPublished - 2007

Publication series

NameCentER Discussion Paper
Volume2007-65

Keywords

  • asymptotic normality
  • regression
  • robust estimation
  • trimming

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