Nonlinear Estimation and Classification

  • David D. Denison
  • Mark H. Hansen
  • Christopher C. Holmes
  • Bani Mallick
  • Bin Yu

Part of the Lecture Notes in Statistics book series (LNS, volume 171)

Table of contents

  1. Front Matter
    Pages N2-vii
  2. Introduction

    1. David D. Denison, Mark H. Hansen, Christopher C. Holmes, Bani Mallick, Bin Yu
      Pages 1-5
  3. Longer Papers

    1. Front Matter
      Pages 7-7
    2. Hyeokho Choi, Richard G. Baraniuk
      Pages 9-29
    3. Harri Kiiveri, Peter Caccetta, Norm Campbell, Fiona Evans, Suzanne Furby, Jeremy Wallace
      Pages 49-62
    4. Peter Bickel, Chao Chen, Jaimyoung Kwon, John Rice, Pravin Varaiya, Erik van Zwet
      Pages 63-81
    5. Jin Cao, William S. Cleveland, Dong Lin, Don X. Sun
      Pages 83-109
    6. Sayan Mukherjee, Ryan Rifkin, Tomaso Poggio
      Pages 111-128
    7. Grace Wahba, Yi Lin, Yoonkyung Lee, Hao Zhang
      Pages 129-147
    8. Dragos D. Margineantu, Thomas G. Dietterich
      Pages 173-188
    9. Jianhua Z. Huang, Charles J. Stone
      Pages 213-233
  4. Shorter Papers

    1. Front Matter
      Pages 235-235
    2. Mário A. T. Figueiredo
      Pages 237-247
    3. Eric D. Kolaczyk, Robert D. Nowak
      Pages 249-259
    4. Maarten Jansen
      Pages 261-271
    5. Charles Kooperberg, Charles J. Stone
      Pages 285-295
    6. Katherine S. Pollard, Mark J. van der Laan
      Pages 307-321
    7. Jörg Rahnenführer
      Pages 323-332
    8. Ingo Ruczinski, Charles Kooperberg, Michael LeBlanc
      Pages 333-343
    9. Servane Gey, Elodie Nedelec
      Pages 369-379
    10. Sebastian Döhler, Ludger Rüschendorf
      Pages 381-392
    11. Angelika van der Linde
      Pages 417-427
    12. Vin de Silva, Joshua B. Tenenbaum
      Pages 453-465
    13. Maria De Iorio, Peter Müller, Gary L. Rosner, Steven N. MacEachern
      Pages 467-474
  5. Back Matter
    Pages 475-477

About this book


Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.


ANOVA Estimator data analysis probability statistical inference statistics time series

Editors and affiliations

  • David D. Denison
    • 1
  • Mark H. Hansen
    • 2
  • Christopher C. Holmes
    • 1
  • Bani Mallick
    • 3
  • Bin Yu
    • 4
  1. 1.Department of MathematicsImperial CollegeLondonUK
  2. 2.Room 2C283 Bell LaboratoriesLucent TechnologiesMurray HillUSA
  3. 3.Statistical DepartmentTexas A&M UniversityCollege StationUSA
  4. 4.Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95471-4
  • Online ISBN 978-0-387-21579-2
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site