TY - JOUR
T1 - PLS regression algorithms in the presence of nonlinearity
AU - Cook, R. Dennis
AU - Forzani, Liliana
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - It has long been emphasized that standard PLS regression algorithms like NIPALS and SIMPLS are not suitable for regressions in which there is a nonlinear relationship between the response and the predictors. We show that this conclusion, while strictly true, fails to recognize that aspects of these algorithms remain serviceable in the presence of nonlinearity. In particular, the dimension reduction step of these standard algorithms is serviceable under linear and nonlinear relationships, while the predictive step is not. Additionally, we propose graphical methods for diagnosing nonlinearity, develop a novel method of nonlinear prediction based on reduced predictors arising from standard PLS regression algorithms and demonstrate the effectiveness of our approach in two case studies.
AB - It has long been emphasized that standard PLS regression algorithms like NIPALS and SIMPLS are not suitable for regressions in which there is a nonlinear relationship between the response and the predictors. We show that this conclusion, while strictly true, fails to recognize that aspects of these algorithms remain serviceable in the presence of nonlinearity. In particular, the dimension reduction step of these standard algorithms is serviceable under linear and nonlinear relationships, while the predictive step is not. Additionally, we propose graphical methods for diagnosing nonlinearity, develop a novel method of nonlinear prediction based on reduced predictors arising from standard PLS regression algorithms and demonstrate the effectiveness of our approach in two case studies.
KW - Central mean subspace
KW - Envelopes
KW - Graphical diagnostics
KW - Krylov sequences
KW - NIPALS
KW - SIMPLS
UR - http://www.scopus.com/inward/record.url?scp=85105309734&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105309734&partnerID=8YFLogxK
U2 - 10.1016/j.chemolab.2021.104307
DO - 10.1016/j.chemolab.2021.104307
M3 - Article
AN - SCOPUS:85105309734
SN - 0169-7439
VL - 213
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
M1 - 104307
ER -