Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators: Theory and Practice: 997 (Wiley Series in Probability and Statistics). This item is unavailable.
Language: English
Published by Wiley, 2015
Series: Book 273 of 358 - Wiley Series in Probability and Statistics
- Softcover
- Used

Seller: WorldofBooks, Goring-By-Sea, WS, United KingdomWorldofBooks
AbeBooks seller since March 16, 2007
Condition: Used - Very good
US$ 7.71
Item description from seller
The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Seller Inventory # GOR013325836
- Title
- Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators: Theory and Practice: 997 (Wiley Series in Probability and Statistics)
- Author
- Hsing
- Publisher
- Wiley
- Publication year
- 2015
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0470016914
- ISBN 13
- 9780470016916
- Item weight
- 590 grams
- Series
- Book 273 of 358: Wiley Series in Probability and Statistics
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).
The self–contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self–adjoint and non self–adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis.
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.
"Synopsis" may belong to another edition of this title.
About the Author
Randall Eubank Professor Emeritus, School of Mathematical and Statistical Sciences, Arizona State University, USA. Professor Eubank is well know and respected in the functional data analysis (FDA) field. He has published numerous papers on the subject and is a regular invited speaker at key meetings.
"About the title" may belong to another edition of this title.