Smoothing Methods in Statistics (Springer Series in Statistics)

Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.

This book surveys the uses of smoothing methods in statistics. The coverage has an applied focus and is very broad, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, k categorical data smoothing, and applications of smoothing to other areas of statistics.