Robust Regression Quantiles with Censored Data
نویسندگان
چکیده
In this paper we propose a method to robustly estimate linear regression quantiles with censored data. We adjust the estimator recently developed by Portnoy by replacing the Koenker-Bassett regression quantiles with the regression depth quantiles. The resulting optimization problem is solved iteratively over a set of grid points. We show on some examples that, contrary to the Koenker-Bassett approach, this estimator can resist bad leverage points.
منابع مشابه
Censored depth quantiles
Quantile regression is a wide spread regression technique which allows to model the entire conditional distribution of the response variable. A natural extension to the case of censored observations was introduced by Portnoy (2003) using a reweighting scheme based on the Kaplan-Meier estimator. We apply the same ideas on the depth quantiles defined in Rousseeuw and Hubert (1999). This leads to ...
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