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Titolo:
A MULTIVARIATE PRINCIPAL COMPONENT REGRESSION ANALYSIS OF NIR DATA
Autore:
SUN JG;
Indirizzi:
HARVARD UNIV,SCH PUBL HLTH,DEPT BIOSTAT,677 HUNTINGTON AVE BOSTON MA 02115
Titolo Testata:
Journal of chemometrics
fascicolo: 1, volume: 10, anno: 1996,
pagine: 1 - 9
SICI:
0886-9383(1996)10:1<1:AMPCRA>2.0.ZU;2-0
Fonte:
ISI
Lingua:
ENG
Keywords:
MULTIVARIATE REGRESSION; NEAR-INFRARED DATA; PRINCIPAL COMPONENT REGRESSION; ROOT MEAN SQUARE ERROR OF PREDICTION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
13
Recensione:
Indirizzi per estratti:
Citazione:
J.G. Sun, "A MULTIVARIATE PRINCIPAL COMPONENT REGRESSION ANALYSIS OF NIR DATA", Journal of chemometrics, 10(1), 1996, pp. 1-9

Abstract

The analysis of near-infrared (NIR) data arising from NIR experimentsin which there exists more than one response variable of interest is discussed,with focus on the investigation of the relationship of response variables. A multivariate regression procedure based on principal component regression (PCR), one of the most commonly used methods in NIR analysis, is described. The presented method gives a simultaneous analysis of response variables of interest and is referred to as multivariate principal component regression (MPCR). The idea behind MPCR is the same as that behind PCR, but MPCR could serve as a tool to study the relationship of response variables. MPCR also makes use of the correlation information of the response variables and thus could save a great of computational effort if the response variables are highly correlated. To illustrate MPCR, its application to a set of NIR data arising from an NIR experiment is briefly discussed.

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Documento generato il 03/07/20 alle ore 00:34:40