The statistical principal component regression (PCR) and chemometric partia
l least squares regression (PLSR) algorithms based on latent variables (LV)
modeling are effective tools for handling ill-conditioned regression data.
In many process related cases the data form time series, and it may then b
e possible to improve the prediction/estimation results by utilizing the au
tocorrelation in the observations. This can be done by use of estimators fo
und from experimental data by use of a combination of statistical/chemometr
ic and system identification methods. In important industrial cases, the re
sponse variables are product qualities which also in the experimental data
are sampled at a low and possibly irregular rate, while the regressor varia
bles are sampled at a higher rate. After a discussion of the options availa
ble, the paper shows how the autocorrelation of the regressor variables in
such multirate sampling cases may be utilized by identification of latent v
ariables based output error (LV + OE) estimators. An example using acoustic
power spectrum regressor data is finally presented.