This book shares original, peer-reviewed research on PLS that is an abbreviation for Partial Least Squares and is also sometimes expanded as projection to latent structures. This is an approach for modeling relations between data matrices of different types of variables measured on the same set of objects. The twenty-two papers in this volume provide a comprehensive overview of the current state of the most advanced research related to PLS and related methods. These exciting theoretical developments range from partial least squares regression and correlation, component based path modeling to regularized regression and subspace visualization. These contributions also included a large variety of PLS approaches such as PLS metamodels, variable selection, sparse PLS regression, distance based PLS, significance vs. reliability, and non-linear PLS. Finally, these contributions applied PLS methods to data originating from the traditional econometric/economic data to genomics data, brain images, information systems, epidemiology, and chemical spectroscopy.
ESPOSITO VINZI, V. [Ed] (2013). New Perspectives in Partial Least Squares and Related Methods. Springer.