The Multiple Facets of Partial Least Squares and Related Methods: PLS, Paris, France, 2014 (Springer Proceedings in Mathematics & Statistics, 173)

ISBN 10: 331982144X / ISBN 13: 9783319821443

Edité par Springer, 2018

A propos de cet article

pp. 316.

N° de réf. du vendeur 26380512269

Détails bibliographiques

• Titre: The Multiple Facets of Partial Least Squares and Related Methods: PLS, Paris, France, 2014 (Springer Proceedings in Mathematics & Statistics, 173)
• Éditeur: Springer
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 331982144X
• ISBN à 13 chiffres: 9783319821443
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

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, andbusiness among both academics and practitioners. The selected chapters here highlight this diversity with applied examples as well as the most recent advances.

À propos de l?auteur

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, namelyfactorial 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.