ITERATIVELY REWEIGHTED PARTIAL LEAST-SQUARES - A PERFORMANCE ANALYSISBY MONTE-CARLO SIMULATION

Citation
Dj. Cummins et Cw. Andrews, ITERATIVELY REWEIGHTED PARTIAL LEAST-SQUARES - A PERFORMANCE ANALYSISBY MONTE-CARLO SIMULATION, Journal of chemometrics, 9(6), 1995, pp. 489-507
Citations number
11
Categorie Soggetti
Chemistry Analytical","Statistic & Probability
Journal title
ISSN journal
08869383
Volume
9
Issue
6
Year of publication
1995
Pages
489 - 507
Database
ISI
SICI code
0886-9383(1995)9:6<489:IRPL-A>2.0.ZU;2-F
Abstract
A robust implementation of partial least squares (PLS) is developed in which the method of iteratively reweighted least squares is adapted f or use with PLS. The result is a PLS algorithm which is robust to outl iers and is easy to implement. Examples and case studies are presented , followed by two Monte Carlo studies designed to explore the behavior of the method. The paper begins with the motivation and intended appl ications for the procedure. A discussion is given of the method of ite ratively reweighted least squares (IRLS) for outlier detection. The pr ocedure, given the name IRPLS, is then presented. Three case studies i llustrate how the procedure works on various types of data and how it should be used. The first Monte Carlo study is designed to determine w hether the IRPLS procedure correctly identifies multiple outliers in a wide variety of configurations. The second Monte Carlo study is desig ned to estimate the breakdown bound of the procedure.