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Statistische Methoden zur Prognose (Wiley-Serie in Wahrscheinlich
by Bovas Abraham; Johannes Ledolter | HC | Good
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eBay-Artikelnr.:146708093560
Artikelmerkmale
- Artikelzustand
- Gut
- Hinweise des Verkäufers
- Binding
- Hardcover
- Book Title
- Statistical Methods for Forecasting (Wiley Series in...
- Weight
- 1 lbs
- Product Group
- Book
- IsTextBook
- Yes
- ISBN
- 9780471867647
Über dieses Produkt
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471867640
ISBN-13
9780471867647
eBay Product ID (ePID)
1104429
Product Key Features
Number of Pages
464 Pages
Language
English
Publication Name
Statistical Methods for Forecasting
Subject
Future Studies, Probability & Statistics / General
Publication Year
1983
Type
Textbook
Subject Area
Mathematics, Social Science
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
28.7 Oz
Item Length
8.3 in
Item Width
6.1 in
Additional Product Features
Edition Number
1
Intended Audience
Scholarly & Professional
LCCN
83-007006
Dewey Edition
19
Series Volume Number
179
Illustrated
Yes
Dewey Decimal
519.54
Table Of Content
Introduction and Summary. The Regression Model and Its Application In Forecasting. Regression and Exponential Smoothing Methods to Forecast Non-seasonal Time Series. Regression and Exponential Smoothing Methods to Forecast Seasonal Time Series. Stochastic Time Series Models. Seasonal Autoregressive Integrated Moving Average Models. Relationships Between Forecasts from General Exponential Smoothing and Forecasts from Arima Time Series Models. Special Topics. References. Exercises. Data Appendix. Table Appendix. Index.
Synopsis
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. 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. "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.' It does just that!" -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." -Choice Statistical 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., 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. 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. "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.' It does just that " -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." -Choice Statistical 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., Econometric Analysis by Control Methods Gregory C. Chow Reports on new developments in the techniques and applications of stochastic control in economics that have token place since the author's Analysis and Control of Dynamic Economic Systems (Wiley, 1975). Includes techniques tailored to nonlinear, simultaneous-equation models in economics, and a guide to a computer program for finding optimal control solutions; control techniques for the analysis and formulation of economic policies and the comparison of econometric models; estimation and control of econometric models under the assumption of rational expectations; and the application of stochastic control methods to models in continuous time. 1981 320 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that: aid in the systematic location of data points that are unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data; help to identify the variables involved in each; pinpoint estimated coefficients that are potentially the most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action. Wiley Series in Probability and Mathematical Statistics. 1980 292 pp. Forecasting with Univariate Box-Jenkins Models Concepts and Cases Alan Pankratz Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using red data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another. 1983 560 pp.
LC Classification Number
QA279.2
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