Kernel Smoothing
Language: English
Published by Taylor and Francis Ltd, GB, 1994
Series: Book 5 of 110 - ISSN
- Hardcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 299.50
Quantity: Over 20 available
Add to basketItem description from seller
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local averaging or smoothing is performed with respect to a kernel function. This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernel Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.More information on the book, and the accompanying R package can be found here.
Seller Inventory # LU-9780412552700
- Title
- Kernel Smoothing
- Author
- M.P. Wand, M.C. Jones
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 1994
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 0412552701
- ISBN 13
- 9780412552700
- Item weight
- 460 grams
- Series
- Book 5 of 110: ISSN
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets.The basic principle is that local averaging or smoothing is performed with respect to a kernel function.
This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated by the authors' focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail.
Kernel Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.
More information on the book, and the accompanying R package can be found here.
"Synopsis" may belong to another edition of this title.
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"About the title" may belong to another edition of this title.
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