Springer Proceedings in Mathematics & Statistics

The Multiple Facets of Partial Least Squares and Related Methods

PLS, Paris, France, 2014

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 
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About this book

This volume presents state of the art theories, new developments, and important applications of Partial Least Square (PLS) methods. The text begins with the invited communications of current leaders in the field who cover the history of PLS, an overview of methodological issues, and recent advances in regression and multi-block approaches. The rest of the volume comprises selected, reviewed contributions from the 8th International Conference on Partial Least Squares and Related Methods held in Paris, France, on 26-28 May, 2014. They are organized in four coherent sections: 1) new developments in genomics and brain imaging, 2) new and alternative methods for multi-table and path analysis, 3) advances in partial least square regression (PLSR), and  4) partial least square path modeling (PLS-PM) breakthroughs and applications. PLS methods are very versatile methods that are now used in areas as diverse as engineering, life science, sociology, psychology, brain imaging, genomics, and business among both academics and practitioners. The selected chapters here highlight this diversity with applied examples as well as the most recent advances.

About the authors

Hervé Abdi—a former Fulbright scholar and resident of the Paris Institute for Advanced Studies—is professor of cognitive neuroscience at the University of Texas at Dallas. His recent research interests focus on the development of analytical and statistical tools for the analysis of very large data sets as generated, for example, by brain imaging, genetic, genomic, and sensory science research. In addition to Partial Least Squares (PLS) models, he is also particularly interested in variable selection and in multi-modal, multi-way, and multi-block methods. He is the author of more than 250 publications (including 12 books) in these domains. 

Vincenzo Esposito Vinzi—a former President of the International Society for Business and Industrial Statistics (ISBIS)—is professor of statistics and Dean of the Faculty at the ESSEC Business School in Paris and Singapore. His research interests focus mainly on methodological developments in multivariate statistics, namely factorial methods, structural equation modeling, PLS  regression, and predictive path modeling that are relevant to various applications in business and industry, sensory data, social surveys, and the measurement of customer satisfaction for commodities and public services.

Giorgio Russolillo is associate professor of statistics at the Conservatoire National des Arts et Métiers (CNAM) of Paris where he is a member of the CEDRIC research group. His research interests involve multivariate data analysis models and methods applied to social and life sciences. Much of his current research focuses on PLS algorithms.

Gilbert Saporta—a former president of the International  Association for Statistical Computing—is emeritus professor of applied  statistics at the Conservatoire National des Arts et Métiers (CNAM) of Paris. His research interests are in exploratory multivariate analysis: functional data analysis including functional PLS, sparse methods, and supervised classification. 

Laura Trinchera is assistant professor of computational statistics at the NEOMA business school (Rouen Campus, France).  Her research interests cover multivariate analyses, structural equation models (SEM), PLS methods. She is also interested in variable selection in regression models.


Table of contents (22 chapters)

  • Partial Least Squares for Heterogeneous Data

    Bühlmann, Peter

    Pages 3-15

  • On the PLS Algorithm for Multiple Regression (PLS1)

    Takane, Yoshio (et al.)

    Pages 17-28

  • Extending the Finite Iterative Method for Computing the Covariance Matrix Implied by a Recursive Path Model

    Hadri, Zouhair El (et al.)

    Pages 29-43

  • Which Resampling-Based Error Estimator for Benchmark Studies? A Power Analysis with Application to PLS-LDA

    Boulesteix, Anne-Laure

    Pages 45-57

  • Path Directions Incoherence in PLS Path Modeling: A Prediction-Oriented Solution

    Dolce, Pasquale (et al.)

    Pages 59-70

Buy this book

eBook n/a
  • ISBN 978-3-319-40643-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Hardcover n/a
  • ISBN 978-3-319-40641-1
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
The Multiple Facets of Partial Least Squares and Related Methods
Book Subtitle
PLS, Paris, France, 2014
Editors
  • Hervé Abdi
  • Vincenzo Esposito Vinzi
  • Giorgio Russolillo
  • Gilbert Saporta
  • Laura Trinchera
Series Title
Springer Proceedings in Mathematics & Statistics
Series Volume
173
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-40643-5
DOI
10.1007/978-3-319-40643-5
Hardcover ISBN
978-3-319-40641-1
Series ISSN
2194-1009
Edition Number
1
Number of Pages
XIV, 316
Number of Illustrations and Tables
29 b/w illustrations, 39 illustrations in colour
Topics