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Model and estimators for partial least squares regression
Author(s) -
Helland Inge Svein,
Sæbø Solve,
Almøy Trygve,
Rimal Raju
Publication year - 2018
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.3044
Subject(s) - partial least squares regression , estimator , mathematics , statistics , ordinary least squares , regression analysis , univariate , bayes' theorem , generalized least squares , bayesian probability , multivariate statistics
Partial least squares (PLS) regression has been a very popular method for prediction. The method can in a natural way be connected to a statistical model, which now has been extended and further developed in terms of an envelope model. Concentrating on the univariate case, several estimators of the regression vector in this model are defined, including the ordinary PLS estimator, the maximum likelihood envelope estimator, and a recently proposed Bayes PLS estimator. These are compared with respect to prediction error by systematic simulations. The simulations indicate that Bayes PLS performs well compared with the other methods.