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Statistical Methods for Forecasting epub download

by Bovas Abraham


Statistical Methods for Forecasting is a comprehensive, readabletreatment of statistical methods and models used to produceshort-term forecasts

Statistical Methods for Forecasting is a comprehensive, readabletreatment of statistical methods and models used to produceshort-term forecasts. The interconnections between the forecastingmodels and methods are thoroughly explained, and the gap betweentheory and practice is successfully bridged.

Bovas Abraham, Johannes Ledolter. 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)

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Электронная книга "Statistical Methods for Forecasting", Bovas Abraham, Johannes Ledolter

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It is considered by some to be one of the 20th century's most influential books on statistical methods, together with his The Design of Experiments (1935). According to Conniffe (1991, p. 87),

About the authors Bovas Abraham is Associate Professor in the Department of Statistics and Actuarial Science, at the . Dr. Ledolter is also coauthor of Forecasting Using Leading Indicators. in statistics from the University of Wisconsin, Madison

About the authors Bovas Abraham is Associate Professor in the Department of Statistics and Actuarial Science, at the University of Waterloo, Ontario, Canada. He is a member of the American Statistical Association, the American Society for Duality Control, the Canadian Statistical Association and a Fellow of the Royal Statistical Society. Abraham received his P. in statistics from the University of Wisconsin, Madison. Country of Publication.

oceedings{alMF, title {Statistical Methods for Forecasting}, author {Bovas Abraham and Johannes . 3. Regression and Exponential Smoothing Methods to Forecast Nonseasonal Time Series.

oceedings{alMF, title {Statistical Methods for Forecasting}, author {Bovas Abraham and Johannes Ledolter}, year {1983} }. Bovas Abraham, Johannes Ledolter. 1. Introduction and Summary. 2. The Regression Model and Its Application in Forecasting. 4. Regression and Exponential Smoothing Methods to Forecast Seasonal Time Series. 5. Stochastic Time Series Models. 6. Seasonal Autoregressive Integrated Moving Average Models.

models,, Be able to understand the R output of a forecasting study.

Course Objectives: By the conclusion of this course, students should have achieved the following objectives:, Understand how to use regression models for forecasting and their limitations,, Understand how to use descriptive approaches for forecasting and their limitations,, Have a familiarity with the Box-Jenkins model approach to time series data,, Be able to analyze a given time series using appropriate forecasting methods and. models,, Be able to understand the R output of a forecasting study.

The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on itsadvertising cover: 'Bridging the gap between introductory,descriptive approaches and highly advanced theoretical treatises,it provides a practical, intermediate level discussion of a varietyof 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 modelsthat can be used to produce short-term forecasts, this book haswide-ranging applications. It could be used in the context of astudy of regression, forecasting, and time series analysis by PhDstudents; or to support a concentration in quantitative methods forMBA students; or as a work in applied statistics for advancedundergraduates."-ChoiceStatistical Methods for Forecasting is a comprehensive, readabletreatment of statistical methods and models used to produceshort-term forecasts. The interconnections between the forecastingmodels and methods are thoroughly explained, and the gap betweentheory and practice is successfully bridged. Special topics arediscussed, such as transfer function modeling; Kalman filtering;state space models; Bayesian forecasting; and methods for forecastevaluation, comparison, and control. The book provides time series,autocorrelation, and partial autocorrelation plots, as well asexamples and exercises using real data. Statistical Methods forForecasting serves as an outstanding textbook for advancedundergraduate and graduate courses in statistics, business,engineering, and the social sciences, as well as a workingreference for professionals in business, industry, and government.

Statistical Methods for Forecasting epub download

ISBN13: 978-0471769873

ISBN: 0471769878

Author: Bovas Abraham

Category: Math and Science

Subcategory: Mathematics

Language: English

Publisher: Wiley-Interscience; 2nd edition (August 12, 2013)

Pages: 445 pages

ePUB size: 1137 kb

FB2 size: 1374 kb

Rating: 4.3

Votes: 501

Other Formats: lrf mobi docx lrf

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