Lynne J. Williams

TL;DR: Principal component analysis (PCA) as discussed by the authors is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables, and its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and display the pattern of similarity of the observations and of the variables as points in maps.

TL;DR: For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation and are presented with small numerical examples and typical applications in neuroimaging.

TL;DR: This article presents MFA, reviews recent extensions, and illustrates it with a detailed example that shows the common factor scores could be obtained by replacing the original normalized data tables by the normalized factor scores obtained from the PCA of each of these tables.

TL;DR: This paper presents and illustrates PLSC and PLSR and shows how these descriptive multivariate analysis techniques can be extended to deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.

TL;DR: Statis as discussed by the authors is an extension of Principal Component Analysis (PCA) tailored to handle multiple data tables that measure sets of variables collected on the same observations, or, alternatively, as in a variant called dual-STATIS, where the same variables are measured on different sets of observations.