Support vector censored quantile regression under random censoring

被引：21

作者：

Shim, Jooyong ^{[2
]}

Hwang, Changha ^{[1
]}

机构：

[1] Dankook Univ, Div Informat & Comp Sci, Seoul 140714, South Korea

[2] Catholic Univ Daegu, Dept Appl Sci, Kyungbuk 702701, South Korea

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2009年 / 53卷 / 04期

关键词：

MODELS; SURVIVAL;

D O I：

10.1016/j.csda.2008.10.037

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Censored quantile regression models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper, we propose support vector censored quantile regression (SVCQR) under random censoring using iterative reweighted least squares (IRWLS) procedure based on the Newton method instead of usual quadratic programming algorithms. This procedure makes it possible to derive the generalized approximate cross validation (GACV) method for choosing the hyperparameters which affect the performance of SVCQR. Numerical results are then presented which illustrate the performance of SVCQR using the IRWLS procedure. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：912 / 919

页数：8

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