New Perspectives in Partial Least Squares and Related Methods

  • Herve Abdi
  • Wynne W. Chin
  • Vincenzo Esposito Vinzi
  • Giorgio Russolillo
  • Laura Trinchera

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 56)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Keynotes

    1. Front Matter
      Pages 1-1
    2. Harald Martens, Kristin Tøndel, Valeriya Tafintseva, Achim Kohler, Erik Plahte, Jon Olav Vik et al.
      Pages 3-30
  3. Large Datasets and Genomics

    1. Front Matter
      Pages 79-79
    2. Tahir Mehmood, Lars Snipen
      Pages 95-105
    3. Antonio Ciampi, Lin Yang, Aurélie Labbe, Chantal Mérette
      Pages 107-116
    4. Tzu-Yu Liu, Laura Trinchera, Arthur Tenenhaus, Dennis Wei, Alfred O. Hero
      Pages 117-127
  4. Brain Imaging

    1. Front Matter
      Pages 129-129
    2. Anjali Krishnan, Nikolaus Kriegeskorte, Hervé Abdi
      Pages 131-145
    3. Edith Le Floch, Laura Trinchera, Vincent Guillemot, Arthur Tenenhaus, Jean-Baptiste Poline, Vincent Frouin et al.
      Pages 147-158
    4. Natasa Kovacevic, Hervé Abdi, Derek Beaton, Anthony R. McIntosh
      Pages 159-170
    5. Nathan Churchill, Robyn Spring, Hervé Abdi, Natasa Kovacevic, Anthony R. McIntosh, Stephen Strother
      Pages 171-183
  5. Multiblock Data Modeling

    1. Front Matter
      Pages 185-185
    2. Tommy Löfstedt, Mohamed Hanafi, Johan Trygg
      Pages 209-220

About these proceedings


New Perspectives in Partial Least Squares and Related Methods shares original, peer-reviewed research from presentations during the 2012 partial least squares methods meeting (PLS 2012). This was the 7th meeting in the series of PLS conferences and the first to take place in the USA. PLS 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, which include three invited contributions from our keynote speakers, provide a comprehensive overview of the current state of the most advanced research related to PLS and related methods. Prominent scientists from around the world took part in PLS 2012 and their contributions covered the multiple dimensions of the partial least squares-based methods. These exciting theoretical developments ranged from partial least squares regression and correlation, component based path modeling to regularized regression and subspace visualization. In following the tradition of the six previous PLS meetings, 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. Such a broad and comprehensive volume will also encourage new uses of PLS models in work by researchers and students in many fields.


Multi-block data analysis Partial Least Square genomics large data set regularization structural equation modeling

Editors and affiliations

  • Herve Abdi
    • 1
  • Wynne W. Chin
    • 2
  • Vincenzo Esposito Vinzi
    • 3
  • Giorgio Russolillo
    • 4
  • Laura Trinchera
    • 5
  1. 1.School of Behavioral & Brain SciencesThe University of Texas at DallasRichardsonUSA
  2. 2.Department of Decision and Information SystemsUniversity of HoustonHoustonUSA
  3. 3.ESSEC Business School of ParisCergy-Pontoise CedexFrance
  4. 4.CNAMParisUSA
  5. 5.Rouen Business SchoolRouenFrance

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-8282-6
  • Online ISBN 978-1-4614-8283-3
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017
  • About this book
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