<p>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.</p><p>This book provides uninitiated readers with a feeling for the principles, applications
Kernel Smoothing
β Scribed by M. P. Wand, M. C. Jones (auth.)
- Publisher
- Springer US
- Year
- 1995
- Tongue
- English
- Leaves
- 224
- Series
- Monographs on Statistics and Applied Probability 60
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Content:
Front Matter....Pages i-xii
Introduction....Pages 1-9
Univariate kernel density estimation....Pages 10-57
Bandwidth selection....Pages 58-89
Multivariate kernel density estimation....Pages 90-113
Kernel regression....Pages 114-145
Selected extra topics....Pages 146-171
Back Matter....Pages 172-212
π SIMILAR VOLUMES
Methods of kernel estimates represent one of the most effective nonparametric smoothing techniques. These methods are simple to understand and they possess very good statistical properties. This book provides a concise and comprehensive overview of statistical theory and in addition, emphasis is giv
<p><p>This is the first book to provide an accessible and comprehensive introduction to a newly developed smoothing technique using asymmetric kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics addressed include t
Kernel smoothing has greatly evolved since its inception to become an essential methodology in the Data Science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solution