Influence properties of trilinear partial least squares regression

In this article we derive an algorithm to compute the influence function for tri-PLS1 regression. Based on the influence function, we propose the squared influence diagnostic plot to assess the influence of individual samples on calibration and prediction. We illustrate the applicability of the squared influence diagnostic plot for tri-PLS1 to two different data sets which have previously been reported in literature. Finally we note that from the influence function, a new estimate of prediction variance can be obtained. Copyright (c) 2005 John Wiley & Sons, Ltd.