Smoothing Methods in Statistics

Jeffrey S. Simonoff

ISBN 10: 0387947167 / ISBN 13: 9780387947167

Edité par Springer New York, Springer New York, 1996

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue.

N° de réf. du vendeur 9780387947167

Détails bibliographiques

• Titre: Smoothing Methods in Statistics
• Éditeur: Springer New York, Springer New York
• Date de parution: 1996
• Langue: anglais
• ISBN à 10 chiffres: 0387947167
• ISBN à 13 chiffres: 9780387947167
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.