Principal covariates regression: Part I. Theory

https://doi.org/10.1016/0169-7439(92)80100-IGet rights and content

Abstract

De Jong, S. and Kiers, H.A.L., 1992. Principal covariates regression. Part I. Theory. Chemometrics and Intelligent Laboratory Systems, 14: 155–164.

A method for multivariate regression is proposed that is based on the simultaneous least-squares minimization of Y residuals and X residuals by a number of orthogonal X components. By lending increasing weight to the X variables relative to the Y variables, the procedure moves from ordinary least-squares regression to principal component regression, forming a relatively simple alternative for continuum regression. Analogies and differences with this and other biased regression techniques are discussed. Possible extensions to multi-block problems and nonlinear relationships are indicated.

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