Statistical Methods for Forecasting (Wiley Series in Probability and Statistics) (9780471769873) Bovas Abraham, Johannes Ledolter

Product Details:
Paperback: 472 pages
Publisher: Wiley-Interscience; 2nd edition (September 28, 2005)
Language: English
ISBN-10: 0471769878
ISBN-13: 978-0471769873

Product Dimensions:

8.8 x 6 x 0.9 inches
Shipping Weight: 1.3 pounds (View shipping rates and policies)

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation dental implant prosthetics carl e misch.torrents. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists dental implant prosthetics carl e misch.torrents.”This book, it must be said, lives up to the words on its advertising cover: ‘Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice dental implant prosthetics carl e misch.torrents.’ It does just that dental implant prosthetics carl e misch.torrents!”-Journal of the Royal Statistical Society”A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates.”-ChoiceStatistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

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