Model averaging in calibration of near-infrared instruments with correlated high-dimensional data.

Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected variables is taken into account by weight-combining each estimate of the so-called 'candidate model'. Some studies have shown that MA enables better prediction, even in high-dimensional cases. However, little is known about the model prediction performance at different types of multicollinearity in high-dimensional data.