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- W3122908840 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." @default.
- W3122908840 created "2021-02-01" @default.
- W3122908840 creator A5046146397 @default.
- W3122908840 date "2008-01-01" @default.
- W3122908840 modified "2023-09-27" @default.
- W3122908840 title "Semiparametric Robust Estimation of Truncated and Censored Regression Models" @default.
- W3122908840 hasPublicationYear "2008" @default.
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