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Trimmed Likelihood-based Estimation in Binary Regression Models

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  • Cizek, P.

    (Tilburg University, Center for Economic Research)

Abstract

The binary-choice regression models such as probit and logit are typically estimated by the maximum likelihood method.To improve its robustness, various M-estimation based procedures were proposed, which however require bias corrections to achieve consistency and their resistance to outliers is relatively low.On the contrary, traditional high-breakdown point methods such as maximum trimmed likelihood are not applicable since they induce the separation of data and thus non-identification of estimates by trimming observations.We propose a new robust estimator of binary-choice models based on a maximum symmetrically trimmed likelihood estimator.It is proved to be identified and consistent, and additionally, it does not create separation in the space of explanatory variables as the existing maximum trimmed likelihood.We also discuss asymptotic and robust properties of the proposed method and compare all methods by means of Monte Carlo simulations.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2005-108.

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Date of creation: 2005
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Handle: RePEc:dgr:kubcen:2005108

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

Related research

Keywords: regression analysis; maximum likelihood; binary-choice regression; robust estimation; trimming;

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  1. Croux, Christophe & Flandre, Cécile & Haesbroeck, Gentiane, 2002. "The breakdown behavior of the maximum likelihood estimator in the logistic regression model," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 377-386, December.
  2. Cizek, P., 2004. "General Trimmed Estimation: Robust Approach to Nonlinear and Limited Dependent Variable Models," Discussion Paper 2004-130, Tilburg University, Center for Economic Research.
  3. Marc G. Genton & André Lucas, 2000. "Comprehensive Definitions of Breakdown-Points for Independent and Dependent Observations," Tinbergen Institute Discussion Papers 00-040/2, Tinbergen Institute.
  4. Croux, Christophe & Haesbroeck, Gentiane, 2003. "Implementing the Bianco and Yohai estimator for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 273-295, October.
  5. Pavel Cizek, 2001. "Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models," CERGE-EI Working Papers wp189, The Center for Economic Research and Graduate Education - Economic Institute, Prague.
  6. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
  7. repec:wop:humbsf:2001-100 is not listed on IDEAS
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