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Smoothing methods in statistics by Jeffrey S. Simonoff

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Published by Springer in New York .
Written in English

Subjects:

  • Smoothing (Statistics)

Book details:

Edition Notes

Includes bibliographical references (p. [290]-320) and indexes.

StatementJeffrey S. Simonoff.
SeriesSpringer series in statistics
Classifications
LC ClassificationsQA278 .S526 1996
The Physical Object
Paginationxii, 338 p. ;
Number of Pages338
ID Numbers
Open LibraryOL974751M
ISBN 100387947167
LC Control Number96011742

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  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/5(2). 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. 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, categorical data smoothing, and applications of smoothing to other areas of statistics. The book will be of particular interest to data analysts, 5/5(1). 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.

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Exponential smoothing methods have been around since the s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model . Smoothing Methods in Statistics by Jeffrey S. Simonoff and a great selection of related books, art and collectibles available now at - Smoothing Methods in Statistics Springer Series in Statistics by Simonoff, Jeffrey S - AbeBooks. Cleveland, W.S. and Loader, C. () Smoothing by local regression: principles and methods (with discussion). In Statistical Theory and Computational Aspects of Smoothing, eds. W. Hardle and M.G. Schimek, Physica-Verlag, Heidelberg, ; ; [given in book as Cleveland and Loader ()]. Other Smoothing Methods (Section ) Section describes several sophisticated and useful alternatives to moving average smoothing. The details may seem sketchy, but that's okay because we don’t want to get bogged down in lots of details for those methods.