Partial Least Squares (pls)

Editors: Abdi, H., Esposito Vinzi, V., Russolillo, G., Saporta, G., Trinchera, L. (Eds.)
Integrates theoretical and methodological advances from recognized leaders in the field, including the conference's invited speakers
Important applications in domains such as genomics, brain imaging, sensory analysis, marketing, psychometrics and information systems
Covers PLS regression, PLS path modeling and developments that go beyond PLS specific methods, extending to other component-based and multi-block or multi-way methods

This book pulls together robust practices in Partial Least Squares Structural Equation Modeling (PLS-SEM) from other disciplines and shows how they can be used in the area of Banking and Finance. In terms of empirical analysis techniques, Banking and Finance is a conservative discipline. As such, this book will raise awareness of the potential of PLS-SEM for application in various contexts. PLS-SEM is a non-parametric approach designed to maximize explained variance in latent constructs.

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification.