论文信息 - Principal component regression in NIR analysis: Viewpoints, background details and selection of components

Principal component regression in NIR analysis: Viewpoints, background details and selection of components

In this paper we present formulae for prediction error related to principal component regression (PCR). The difference between PCR and ordinary least‐squares (LS) regression is discussed in relation to these formulae. This discussion is used as a basis for a treatment of PCR in NIR analysis. The theory is illustrated by two examples from NIR analysis.

T. Næs | H. Martens

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