A two-parametric class of predictors in multivariate regression
2007 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 21, no 5-6, 215-226 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2007. Vol. 21, no 5-6, 215-226 p.
joint continuum regression, multivariate prediction, multivariate regression, PCR, PLSR, reduced rank regression, ridge regression, SIMPLS, total least squares
IdentifiersURN: urn:nbn:se:su:diva-24418DOI: 10.1002/cem.1063ISI: 000250098200006OAI: oai:DiVA.org:su-24418DiVA: diva2:197490
Part of urn:nbn:se:su:diva-70252007-09-062007-08-282011-02-16Bibliographically approved