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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.
Kernal 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.
- Sales Rank: #978653 in Books
- Published on: 1994-12-01
- Released on: 1995-01-01
- Original language: English
- Number of items: 1
- Dimensions: 8.50" h x .52" w x 5.51" l, .98 pounds
- Binding: Hardcover
- 224 pages
Most helpful customer reviews
8 of 8 people found the following review helpful.
Excellent book on kernel density methods
By AS
If your work requires the use of kernel density methods, there are three books you must have: (1) Density Estimation by Silverman, (2) Multivariate Density Estimation by David Scott, and (3) Kernel smoothing by Wand & Jones. Of the three, Wand & Jones' book is the best written one. Concepts are explained clearly and the book chapters are organized well. Strongly recommended alongwith the other two.
3 of 4 people found the following review helpful.
An intuitive explanation on an o/w very tough subject.
By A Customer
(From the view point of an economist only) Don't know a thing about kernel smoothing? No problem! This book brilliantly explains the idea of kernel smoothing using English! (You remember how advanced econometrics quickly become an alien language.), with minimum requirement on econometrics.It is a must buy if you are working on this subject.
0 of 0 people found the following review helpful.
Kernel Smoothing
By Ward Kingkade
I knew this book was a classic in the field. I didn't know it was as good as it truly is. The authors are clearly gifted teachers, because from the very beginning they provide very understandable explanations and examples. The mathematics is not elementary, but is kept to a level that ought to make the material accessible to any statistician as well as researchers who want to learn about kernel smoothers. The book was written in the mid 1990s, and should be considered an introduction.
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