Wand, M. P and Jones, M. C. (1994). Kernel Smoothing. Chapman & Hall/CRC Monographs on Statistics & Applied Probability (60). Boca Raton, FL, U.S.: Chapman & Hall.
|Google Scholar:||Look up in Google Scholar|
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.
|Item Type:||Authored Book|
|Copyright Holders:||1995 The Authors|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||11 Mar 2011 10:38|
|Last Modified:||02 Aug 2016 14:00|
|Share this page:|