A randomization test for PLS component selection
During the last two decades, a number of methods have been developed and evaluated for selecting the optimal number of components in a PLS model. In this paper, a new method is introduced that is based on a randomization test. The advantage of using a randomization test is that in contrast to cross validation (CV), it requires no exclusion of data, thus avoiding problems related to data exclusion, for example in designed experiments. The method is tested using simulated data sets for which the true dimensionality is clearly defined and also compared to regularly used methods for 10 real data sets. The randomization test works as a good statistical selection tool in combination with other selection rules. It also works as an indicator when the data require a pre-treatment. Copyright (C) 2007 John Wiley & Sons, Ltd.
randomization test component selection permutation test factor selection latent variable selection partial least squares