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Titolo:
Latent root regression analysis: an alternative method to PLS
Autore:
Bertrand, D; Qannari, E; Vigneau, E;
Indirizzi:
INRA, ENITAA, Unite Sensometr & Chimiometr, F-44322 Nantes 03, France INRA Nantes France 03 Sensometr & Chimiometr, F-44322 Nantes 03, France
Titolo Testata:
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
fascicolo: 2, volume: 58, anno: 2001,
pagine: 227 - 234
SICI:
0169-7439(20011028)58:2<227:LRRAAA>2.0.ZU;2-B
Fonte:
ISI
Lingua:
ENG
Keywords:
multiple linear regression; latent root regression; partial least squares; near-infrared spectroscopy;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
10
Recensione:
Indirizzi per estratti:
Indirizzo: Qannari, E INRA, ENITAA, Unite Sensometr & Chimiometr, Rue Geraudiere,BP 82 225, F-44322 Nantes 03, France INRA Rue Geraudiere,BP 82 225 Nantes France 03 ntes 03, France
Citazione:
D. Bertrand et al., "Latent root regression analysis: an alternative method to PLS", CHEM INTELL, 58(2), 2001, pp. 227-234

Abstract

Several applications are based on the assessment of a linear model linkinga variable y to predictors x(1),x(2)..... x(p). It often occurs that the predictors are collinear which results in a high instability of the model obtained by means of multiple linear regression. Several alternative methods have been proposed in order to tackle this problem. Among these methods Ridge Regression (RR), Principal Component Regression (PCR) and Partial Least Squares (PLS) are the most popular. We discuss another alternative method to Multiple Linear Regression (MLR) called Latent Root Regression (LRR). This method basically shares certain common characteristics with PLS as it derives latent variables to be used as predictors. Like PLS, the dependent variable plays a central role in determining the latent variables. We introduce new properties of latent root regression which give new insight into the determination of a prediction model. The mean squared error for the latent root estimator is explicitly given. Thus, a model may be deter-mined by combining latent root estimators in such a way that the associated mean squared error is minimized. The method is illustrated using two real data sets. (C) 2001 Elsevier Science B.V. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 04/07/20 alle ore 17:53:55