Kernel Smoothing (Monographs on Statistics and Applied Probability)

@book{citeulike:2226990, abstract = {{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.}}, author = {Wand, M. P. and Jones, M. C. }, citeulike-article-id = {2226990}, howpublished = {Hardcover}, isbn = {0412552701}, keywords = {density-estimation}, month = {December}, posted-at = {2008-01-13 17:40:38}, priority = {2}, publisher = {{Chapman \& Hall/CRC}}, title = {Kernel Smoothing (Monographs on Statistics and Applied Probability)}, url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/0412552701}, year = {1994} }

TY - BOOK ID - citeulike:2226990 L3 - citeulike-article-id:2226990 TI - Kernel Smoothing (Monographs on Statistics and Applied Probability) PB - {Chapman & Hall/CRC} SN - 0412552701 N2 - {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.} KW - density-estimation AU - Wand, MP AU - Jones, MC PY - 1994/12/01/ UR - http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0412552701 ER -