ROBUST PRINCIPAL COMPONENTS REGRESSION AS A DETECTION TOOL FOR OUTLIERS

Authors
WALCZAK B MASSART DL
Citation
B. Walczak et Dl. Massart, ROBUST PRINCIPAL COMPONENTS REGRESSION AS A DETECTION TOOL FOR OUTLIERS, Chemometrics and intelligent laboratory systems, 27(1), 1995, pp. 41-54
Citations number
14
Categorie Soggetti
Computer Application, Chemistry & Engineering","Instument & Instrumentation","Chemistry Analytical","Computer Science Artificial Intelligence","Robotics & Automatic Control
Journal title
Chemometrics and intelligent laboratory systems → ACNP
ISSN journal
01697439
Volume
27
Issue
1
Year of publication
1995
Pages
41 - 54
Database
ISI
SICI code
0169-7439(1995)27:1<41:RPCRAA>2.0.ZU;2-1
Abstract
Robust principal components regression procedure based on the ellipsoi dal multivariate trimming (MVT) and the least median of squares (LMS) methods is proposed as an outlier detection tool. The performance of t his approach was evaluated using simulated data randomly contaminated.