AN APPROACH TO INTERVAL ESTIMATION IN PARTIAL LEAST-SQUARES REGRESSION

Although partial least squares regression (PLS) is widely used in chemometrics for quantitative spectral analysis, tittle is known about the distribution of the prediction error from calibration models based on PLS. As a result, we must rely on computationally intensive procedures like bootstrapping to produce confidence intervals for predictions, or, in many cases, we must do with no interval estimates at all, only point estimates. In this paper we present an approach, based on the linearization of the PLS estimator, that allows us to construct approximate confidence intervals for predictions from PLS.