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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.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2007-65.

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Date of creation: 2007
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Handle: RePEc:dgr:kubcen:200765

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Web page: http://center.uvt.nl

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Find related papers by JEL classification:

• C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
• 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; Switching Regression Models
• C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

This paper has been announced in the following NEP Reports:

• NEP-ALL-2007-09-16 (All new papers)

References

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Cited by:

1. Cizek, Pavel, 2008. "Robust and Efficient Adaptive Estimation of Binary-Choice Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 687-696, June.
   
   • Cizek, P., 2007. "Robust and Efficient Adaptive Estimation of Binary-Choice Regression Models," Discussion Paper 2007-12, Tilburg University, Center for Economic Research.

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