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General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models

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Author Info

  • Cizek, P.

    (Tilburg University, Center for Economic Research)

Abstract

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 unifies and extends many existing robust procedures. We derive here the consistency and rate of convergence of the proposed general trimmed estimator under mild -mixing conditions and demon- strate its applicability in nonlinear regression, time series, limited dependent variable models, and panel data.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2004-130.

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Date of creation: 2004
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Handle: RePEc:dgr:kubcen:2004130

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

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Keywords: consistency; regression; robust estimation; trimming;

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References

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  1. Pavel Cizek, 2001. "Robust Estimation with Discrete Explanatory Variables," CERGE-EI Working Papers wp183, The Center for Economic Research and Graduate Education - Economic Institute, Prague.
  2. Andrews, Donald W.K., 1992. "Generic Uniform Convergence," Econometric Theory, Cambridge University Press, vol. 8(02), pages 241-257, June.
  3. Donald W.K. Andrews, 1992. "An Introduction to Econometric Applications of Functional Limit Theory for Dependent Random Variables," Cowles Foundation Discussion Papers 1020, Cowles Foundation for Research in Economics, Yale University.
  4. repec:cup:etheor:v:8:y:1992:i:2:p:241-57 is not listed on IDEAS
  5. Agullo, Jose, 2001. "New algorithms for computing the least trimmed squares regression estimator," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 425-439, June.
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Citations

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Cited by:
  1. Cízek, Pavel, 2011. "Semiparametrically weighted robust estimation of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 774-788, January.
  2. Cizek, P., 2006. "Efficient Robust Estimation of Regression Models (Replaced by DP 2007-87)," Discussion Paper 2006-8, Tilburg University, Center for Economic Research.
  3. Cizek, P., 2008. "Semiparametric Robust Estimation of Truncated and Censored Regression Models," Discussion Paper 2008-34, Tilburg University, Center for Economic Research.
  4. Tabri, Rami Victor, 2014. "Testing for normality in linear regression models using regression and scale equivariant estimators," Economics Letters, Elsevier, vol. 122(2), pages 192-196.
  5. Pavel Čížek, 2013. "Reweighted least trimmed squares: an alternative to one-step estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 22(3), pages 514-533, September.
  6. Cheng, Tsung-Chi, 2011. "Robust diagnostics for the heteroscedastic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1845-1866, April.
  7. Cizek, P., 2005. "Trimmed Likelihood-based Estimation in Binary Regression Models," Discussion Paper 2005-108, Tilburg University, Center for Economic Research.
  8. Chalabi, Yohan / Y. & Wuertz, Diethelm, 2010. "Weighted trimmed likelihood estimator for GARCH models," MPRA Paper 26536, University Library of Munich, Germany.
  9. Neykov, N.M. & Filzmoser, P. & Neytchev, P.N., 2012. "Robust joint modeling of mean and dispersion through trimming," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 34-48, January.
  10. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Discussion Paper 2009-25, Tilburg University, Center for Economic Research.
  11. Pavel Cizek & Wolfgang Härdle, 2006. "Robust Econometrics," SFB 649 Discussion Papers SFB649DP2006-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  12. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.

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