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Theoretical foundations of functional data analysis, with an introduction to linear operators / Tailen Hsing and Randall Eubank.

By: Contributor(s): Material type: TextTextSeries: Wiley series in probability and statisticsPublication details: Chichester : John Wiley and Sons, Inc., c2015.Description: xiii, 333 p. ; 24 cmISBN:
  • 9780470016916 (cloth)
Subject(s): DDC classification:
  • 000SA.07 23 H873
Contents:
1. Introduction-- 2. Vector and function spaces-- 3. Linear operator and functionals-- 4. Compact operators and singular value decomposition-- 5. Perturbation theory-- 6. Smoothing and regularization-- 7. Random elements in a Hilbert space-- 8. Mean and covariance estimation-- 9. Principal components analysis-- 10. Canonical correlation analysis-- 11. Regression-- References-- Indexes.
Summary: Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.
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Includes bibliographical references and indexes.

1. Introduction--
2. Vector and function spaces--
3. Linear operator and functionals--
4. Compact operators and singular value decomposition--
5. Perturbation theory--
6. Smoothing and regularization--
7. Random elements in a Hilbert space--
8. Mean and covariance estimation--
9. Principal components analysis--
10. Canonical correlation analysis--
11. Regression--
References--
Indexes.

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA). This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course.

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