Principal component regression for data containing outliers and missing elements

被引：23
|
作者：
Serneels, Sven [2 ]
Verdonck, Tim [1 ]
机构：
[1] Univ Antwerp, Dept Math & Comp Sci, Agoras Grp, B-2020 Antwerp, Belgium
[2] LS Serv & Consultancy, Edegem, Belgium
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2009年 / 53卷 / 11期
关键词：
MULTIVARIATE REGRESSION; ROBUST; ESTIMATOR; PROJECTION; INFERENCE;
D O I：
10.1016/j.csda.2009.04.008
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：3855 / 3863
页数：9
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