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Item #:
45251415
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators (Wiley Series in Probability and Statistics)
Item #:
45251415
MGA 354300
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A valuable reference for statisticians and researchers interested in the mathematical aspects of functional data analysis.
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What Stands Out
Comprehensive Theory
Offers an in-depth exploration of functional data analysis principles, equipping readers with solid theoretical foundations and advanced understanding of statistical methodologies.
Focused on Applications
Provides practical insights into how linear operators apply in real-world data analysis scenarios, enhancing comprehension and usability for students and professionals alike.
Wiley Expertise
Published by Wiley, a trusted name in academic publishing, ensuring rigorous quality standards and relevance in the field of probability and statistics.
Product Details
| Publisher | Wiley |
| Publication date | May 13, 2015 |
| Edition | 1st |
| Language | English |
| Print length | 368 pages |
| ISBN-10 | 9780470016916 |
| ISBN-13 | 978-0470016916 |
| Item Weight | 1.3 pounds (590 grams) |
| Dimensions | 6.24 x 1.01 x 9.24 inches (15.8 x 2.6 x 23.5 cm) |
| Part of series | Wiley Series in Probability and Statistics |
Who Should Buy?
-
Statisticians
Ideal for statisticians seeking in-depth knowledge of functional data analysis and linear operator applications.
-
Researchers
Beneficial for researchers in various fields requiring advanced statistical concepts and methodologies for functional data.
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Academics
Suitable for graduate students and academics teaching or studying functional data analysis in statistics and mathematics.
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Beginners
Not ideal for beginners with little statistical background as it delves into complex theoretical foundations.
Product Description
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators (Wiley Series in Probability and Statistics)
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Customer Questions & Answers
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Question:
What topics are covered in this book?
Answer: The book covers mathematical concepts related to functional analysis, operator theory, and nonparametric estimation, among others. -
Question:
Who is this book intended for?
Answer: It is intended for statisticians, researchers, and graduate students interested in the mathematical foundations of functional data analysis. -
Question:
Is the content suitable for beginners?
Answer: The book is designed for those with a background in statistics and mathematics, making it more suitable for graduate-level readers.
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Features & Benefits
- Comprehensive coverage of key mathematical concepts in functional data analysis.
- Includes topics like reproducing kernel Hilbert spaces and singular value decomposition.
- Discusses probabilistic foundations from random elements in Hilbert spaces to stochastic processes.
- Explains nonparametric estimation methods including kernel and regularized smoothing.
- Investigates properties of estimators and canonical correlations in functional data.
- Ideal for graduate courses and professionals looking to deepen their understanding.
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