Premium
Path modelling by sequential PLS regression
Author(s) -
Næs T.,
Tomic O.,
Mevik B.H.,
Martens H.
Publication year - 2011
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.1357
Subject(s) - weighting , outlier , computer science , latent variable , interpretation (philosophy) , curse of dimensionality , block (permutation group theory) , regression , simple (philosophy) , regression analysis , residual , artificial intelligence , path (computing) , mathematics , data mining , algorithm , machine learning , statistics , medicine , philosophy , geometry , epistemology , radiology , programming language
This paper presents a new approach to path modelling, based on a sequential multi‐block modelling in latent variables. The approach is explorative and focused on interpretation. The method breaks with standard traditions of estimating all paths using one single modelling. Instead, one separate model is estimated for each endogenous block. Each separate model is constructed by stepwise use of the standard PLS regression on matrices that are orthogonalised with respect to each other. The advantages of the approach are that it can allow for different dimensionality within each block, it is invariant to relative weighting of the blocks and it is based on simple and standard methodology allowing for simple outlier detection, validation and interpretation. No convergence problems are involved and the method can be used for situations with many more variables than samples. An application based on sensory analysis of wines will be used to illustrate the method. Copyright © 2010 John Wiley & Sons, Ltd.