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

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  • 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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  1. repec:cup:etheor:v:8:y:1992:i:2:p:241-57 is not listed on IDEAS
  2. Čížek, Pavel, 2002. "Robust estimation with discrete explanatory variables," SFB 373 Discussion Papers 2002,76, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  3. Andrews, Donald W.K., 1992. "Generic Uniform Convergence," Econometric Theory, Cambridge University Press, vol. 8(02), pages 241-257, June.
  4. 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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Cited by:
  1. 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.
  2. Cheng, Tsung-Chi, 2011. "Robust diagnostics for the heteroscedastic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1845-1866, April.
  3. Cízek, Pavel, 2011. "Semiparametrically weighted robust estimation of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 774-788, January.
  4. Cizek, P., 2006. "Efficient Robust Estimation of Regression Models (Replaced by DP 2007-87)," Discussion Paper 2006-8, Tilburg University, Center for Economic Research.
  5. 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.
  6. Pavel Cizek & Wolfgang Härdle, 2006. "Robust Econometrics," SFB 649 Discussion Papers SFB649DP2006-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  7. Cizek, P., 2005. "Trimmed Likelihood-based Estimation in Binary Regression Models," Discussion Paper 2005-108, Tilburg University, Center for Economic Research.
  8. Cizek, P., 2008. "Semiparametric Robust Estimation of Truncated and Censored Regression Models," Discussion Paper 2008-34, Tilburg University, Center for Economic Research.
  9. Cizek, P., 2010. "Reweighted Least Trimmed Squares: An Alternative to One-Step Estimators," Discussion Paper 2010-91, Tilburg University, Center for Economic Research.
  10. Chalabi, Yohan / Y. & Wuertz, Diethelm, 2010. "Weighted trimmed likelihood estimator for GARCH models," MPRA Paper 26536, University Library of Munich, Germany.
  11. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Discussion Paper 2009-25, Tilburg University, Center for Economic Research.

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