Premium
A two‐parametric class of predictors in multivariate regression
Author(s) -
Björkström Anders,
Sundberg Rolf
Publication year - 2007
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.1063
Subject(s) - partial least squares regression , multivariate statistics , univariate , latent variable , statistics , regression analysis , regression , mathematics , parametric statistics , latent class model , latent variable model , regression diagnostic , bayesian multivariate linear regression
We demonstrate that a number of well‐established multivariate regression methods for prediction are related in that they are special cases of basically one general procedure. We try a more general method based on this procedure with two metaparameters. In a simulation study, based on a latent structure model, we compare this method to ridge regression (RR), multivariate partial least squares regression (PLSR) and repeated univariate PLSR. For most types of data sets studied, all methods do approximately equally well. There are some cases where RR and least squares ridge regression (LSRR) yield larger errors than the other methods, and we conclude that one‐factor methods are not adequate for situations where more than one latent variable are needed to describe the data. Among those based on latent variables, none of the methods tried is superior to the others in any obvious way. Copyright © 2007 John Wiley & Sons, Ltd.