An equivalence relation between parallel calibration and principal component regression | Zendy

Fugate Michael L. | Zendy; Christensen Ronald | Zendy; Hush Don | Zendy; Scovel Clint | Zendy

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An equivalence relation between parallel calibration and principal component regression

Author(s) -

Fugate Michael L.,

Christensen Ronald,

Hush Don,

Scovel Clint

Publication year - 2002

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.683

Subject(s) - principal component regression , chemometrics , principal component analysis , calibration , relation (database) , multivariate statistics , equivalence (formal languages) , regression , mathematics , inverse , statistics , computer science , regression analysis , component (thermodynamics) , bayesian multivariate linear regression , data mining , machine learning , discrete mathematics , physics , geometry , thermodynamics

Multivariate calibration and prediction, when there are few observations and many variables, is an important and unresolved problem in chemometrics. Recently a technique called paralle calibration was introduced as a new and easy to understand calibation method. In this paper we show that parallel calibration is equivalent to a form of principal component regression and to generalized inverse regression. Copyright © 2002 John Wiley & Sons, Ltd.

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