Censored depth quantiles

Authors:
M. Debruyne;M. Hubert;S. Portnoy;K. Vanden Branden
Affiliations:
Department of Mathematics, University Center for Statistics, K.U.Leuven, Celestijnenlaan 200B, B-3001 Leuven, Belgium;Department of Mathematics, University Center for Statistics, K.U.Leuven, Celestijnenlaan 200B, B-3001 Leuven, Belgium;Statistics Department, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA;Department of Mathematics, University Center for Statistics, K.U.Leuven, Celestijnenlaan 200B, B-3001 Leuven, Belgium
Venue:
Computational Statistics & Data Analysis
Year:
2008

Citing 2
Cited 2

Robustness of deepest regression

Journal of Multivariate Analysis
The deepest regression method

Journal of Multivariate Analysis

Quantile regression with doubly censored data

Computational Statistics & Data Analysis
Editorial for the special issue on quantile regression and semiparametric methods

Computational Statistics & Data Analysis

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Abstract

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 has been introduced using a reweighting scheme based on the Kaplan-Meier estimator. The same ideas can be applied to depth quantiles. This leads to regression quantiles for censored data which are robust to both outliers in the predictor and the response variable. For their computation, a fast algorithm over a grid of quantile values is proposed. The robustness of the method is shown in a simulation study and on two real data examples.