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Functional Data Analysis (Springer Series in Statistics)

Functional Data Analysis (Springer Series in Statistics)

Functional Data Analysis (Springer Series in Statistics)
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Functional Data Analysis (Springer Series in Statistics) Hardback - 1997

by Ramsay, J

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hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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Details

  • Title Functional Data Analysis (Springer Series in Statistics)
  • Author Ramsay, J
  • Binding Hardback
  • Edition First printing
  • Condition Used - Good
  • Pages 310
  • Volumes 1
  • Language ENG
  • Publisher Springer, New York
  • Publication date 1997-06
  • Illustrated Yes
  • Bookseller's Inventory # 0387949569.G
  • ISBN 9780387949567 / 0387949569
  • Weight 1.34 lbs (0.61 kg)
  • Dimensions 9.68 x 6.3 x 0.81 in (24.59 x 16.00 x 2.06 cm)
  • Category Mathematics
  • Library of Congress subjects Multivariate analysis
  • Library of Congress Catalogue Number 96054729
  • Dewey Decimal Code 519.5
  • Quantity available 1

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Reader reviews for Functional Data Analysis (Springer Series in Statistics)

From the publisher

Scientists today collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data. Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meterology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields. Much of the material is based on the authors' own work, some of which appears here
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