Censored depth quantiles

被引：5

作者：

Debruyne, M. ^{[1
]}

Hubert, M. ^{[1
]}

Portnoy, S. ^{[2
]}

Branden, K. Vanden ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Math, Univ Ctr Stat, B-3001 Louvain, Belgium

[2] Univ Illinois, Dept Stat, Champaign, IL 61820 USA

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2008年 / 52卷 / 03期

基金：

美国国家科学基金会;

关键词：

regression depth; quantile regression; censoring; robustness;

D O I：

10.1016/j.csda.2007.05.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2007 Elsevier B.V. All rights reserved.

引用

页码：1604 / 1614

页数：11

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