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Iteratively reweighted partial least squares: A performance analysis by monte carlo simulation
Author(s) -
Cummins David J.,
Andrews C. Webster
Publication year - 1995
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180090607
Subject(s) - iteratively reweighted least squares , monte carlo method , outlier , partial least squares regression , computer science , algorithm , least squares function approximation , mathematics , non linear least squares , statistics , artificial intelligence , estimation theory , machine learning , estimator
A robust implementation of partial least squares (PLS) is developed in which the method of iteratively reweighted least squares is adapted for use with PLS. The result is a PLS algorithm which is robust to outliers 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 applications for the procedure. A discussion is given of the method of interatively reweighted least squares (IRLS) for outlier detection. The procedure, given the name IRPLS, is then presented. Three case studies illustrate how the procedure works on various types of data and how it should be used. The first Monte Carlo study is designed to determine whether the IRPLS procedure correctly identifies multiple outliers in a wide variety of configurations. The second Monte Carlo study is designed to estimate the breakdown bound of the procedure.