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Semiparametric Robust Estimation of Truncated and Censored Regression Models

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Author Info

  • Cizek, P.

    (Tilburg University, Center for Economic Research)

Abstract

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semipara- metric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust and relatively imprecise. To improve its performance, we also propose data-adaptive and one-step trimmed estimators. We derive the robust and asymptotic properties of all proposed estimators and show that the one-step estimators (e.g., one-step SCLS) are as robust as GTE and are asymptotically equivalent to the original estimator (e.g., SCLS). The infinite-sample properties of existing and proposed estimators are studied 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 2008-34.

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Date of creation: 2008
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Handle: RePEc:dgr:kubcen:200834

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

Related research

Keywords: Asymptotic normality; censored regression; one-step estimation; robust esti- mation; trimming; truncated regression;

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Cited by:
  1. Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods and Applications, Springer, vol. 21(2), pages 121-137, June.

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