ROBUST PRINCIPAL COMPONENTS REGRESSION AS A DETECTION TOOL FOR OUTLIERS

被引：92

作者：

WALCZAK, B ^{[1
]}

MASSART, DL ^{[1
]}

机构：

[1] FREE UNIV BRUSSELS,INST PHARMACEUT,CHEMOAC,B-1090 BRUSSELS,BELGIUM

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1995年 / 27卷 / 01期

关键词：

D O I：

10.1016/0169-7439(94)00059-R

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 [计算机科学与技术];

摘要：

Robust principal components regression procedure based on the ellipsoidal multivariate trimming (MVT) and the least median of squares (LMS) methods is proposed as an outlier detection tool. The performance of this approach was evaluated using simulated data randomly contaminated.

引用

页码：41 / 54

页数：14

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