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Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators (Wiley Series in Probability and Statistics)
ANG 141
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A valuable reference for statisticians and researchers interested in the mathematical aspects of functional data analysis.
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Productdetails
| Item Weight | 2 lbs (910 grams) |
Voor wie is dit geschikt?
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Statisticians
Ideal for statisticians seeking in-depth knowledge of functional data analysis and linear operator applications.
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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.
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Practitioners
Less useful for practitioners needing quick, practical solutions rather than theoretical insights on data analysis.
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Casual Readers
Not suitable for casual readers seeking light material; it requires focused study and prior knowledge.
PRODUCTBESCHRIJVING
Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators (Wiley Series in Probability and Statistics)
Vragen & Antwoorden van klanten
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vraag:
What topics are covered in this book?
antwoord: The book covers mathematical concepts related to functional analysis, operator theory, and nonparametric estimation, among others. -
vraag:
Who is this book intended for?
antwoord: It is intended for statisticians, researchers, and graduate students interested in the mathematical foundations of functional data analysis. -
vraag:
Is the content suitable for beginners?
antwoord: The book is designed for those with a background in statistics and mathematics, making it more suitable for graduate-level readers.
Probability & Statistics Editorial Review
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ANG 141
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Kenmerken en voordelen
- 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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