Kernel Smoothing

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. Nonparametric regression and density estimation are two of the most fundamental problems to which kernel smoothing provides a simple and effective solution.

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.

Get Kernel Smoothing by at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Taylor & Francis Ltd and it has pages.