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Wiley Series in Probability and Statistics Ser.: Time Series Analysis :...
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eBay item number:285378346980
Item specifics
- Condition
- ISBN
- 9780470272848
- Subject Area
- Mathematics
- Publication Name
- Time Series Analysis : Forecasting and Control
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9.4 in
- Subject
- Probability & Statistics / Stochastic Processes, Probability & Statistics / General, Probability & Statistics / Time Series, Applied
- Publication Year
- 2008
- Series
- Wiley Series in Probability and Statistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.5 in
- Item Weight
- 42.8 Oz
- Item Width
- 6.3 in
- Number of Pages
- 784 Pages
About this product
Product Information
- Time Series Analysis is the seminal book of choice for a clear and practical understanding of time series as it relates to the applied areas of forecasting and quality control. - This new edition is a revision of a classic and authoritative book that has been the model for most books on the topic written since 1970.
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0470272848
ISBN-13
9780470272848
eBay Product ID (ePID)
63177228
Product Key Features
Number of Pages
784 Pages
Language
English
Publication Name
Time Series Analysis : Forecasting and Control
Publication Year
2008
Subject
Probability & Statistics / Stochastic Processes, Probability & Statistics / General, Probability & Statistics / Time Series, Applied
Type
Textbook
Subject Area
Mathematics
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1.5 in
Item Weight
42.8 Oz
Item Length
9.4 in
Item Width
6.3 in
Additional Product Features
Edition Number
4
Intended Audience
Scholarly & Professional
LCCN
2007-044569
Dewey Edition
23
Reviews
'The book follows faithfully the style of the original edition. The approach is heavily motivated by real world time series, and by developing a complete approach to model building, estimation, forecasting and control.' ( Mathematical Reviews , 2009) "I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." ( MAA Reviews , January 13, 2009) "I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." ( MAA Reviews, January 2009), "I think the book is very valuable and useful to graduate students in statistics, mathematics, engineering, and the like. Also, it could be of tremendous help to practioners. Even though the book is written in a clear, easy to follow narrative style with plenty of illustrations, one should nevertheless have a sufficient knowledge of graduate level mathematical statistics. By reading and understanding the book one should, in the end, feel very confident in time series and analysis." ( MAA Reviews, January 2009)
Series Volume Number
734
Illustrated
Yes
Dewey Decimal
519.5/5
Lc Classification Number
Qa280.B67 2008
Table of Content
Preface to the Fourth Edition.Preface to the Third Edition.1. Introduction.1.1 Five Important Practical Problems.1.2 Stochastic and Deterministic Dynamic Mathematical Models.Part One: Stochastic Models and Their Forecasting.2. Autocorrelation Function and Spectrum of Stationary Processes.2.1 Autocorelation Properties of Stationary Models.2.2 Spectral Properties of Stationary Models.3. Linear Stationary Models.3.1 General Linear Process.3.2 Autoregressive Processes.3.3 Moving Average Processes.3.4 Mixed Autoregressive-Moving Average Processes4. Linear Nonstationary Models.4.1 Autoregressive Integrated Moving Average Processes.4.2 Three Explicit Forms for the Autoregressive Integrated Moving Average Model.4.3 Integrated Moving Average Processes.5. Forecasting.5.1 Minimum Mean Square Error Forecasts and Their Properties.5.2 Calculating and Updating Forecasts.5.3 Forecast Function and Forecast Wrights.5.4 Example of Forecast Functions and Their Updating.5.5 Use of State-Space Model Formulation for Exact Forecasting.5.6 Summary.Part Two: Stochastic Model Building.6. Model Identification.6.1 Objective of Identification.6.2 Indetification Techniques.6.3 Initial Estimates for the Parameters.6.4 Model Multiplicity.7. Model Estimation.7.1 Study of the Likelihood and Sum-of-Squares Functions.7.2 Nonlinear Estimation.7.3 Some Estimation Results for Specific Models.7.4 Likelihood Function Based on the State-Space Model.7.5 Unit Roots in Arima Models.7.6 Estimation Using Bayes's Theorem.8. Model Diagnostic Checking.8.1 Checking the Stochastic Model.8.2 Diagnostic Checks Applied to Residuals.8.3 Use of Residuals to Modify the Model.9. Seasonal Models.9.1 Parsimonious Models for Seasonal Time Series.9.2 Representation of the Airline Data by a Multiplicative.9.3 Some Aspects of More General Seasonal ARIMA Models.9.4 Structural Component Models and Deterministic Seasonal Components.9.5 Regression Models with Time Error Terms.10. Nonlinear and Long Memory Models.10.1 Autoregressive Conditional Heteroscedastic (ARCH) Models.10.2 Nonlinear Time Series Models.10.3 Long memory Time Series Processes.Part Three: Transfer Function and Multivariate Model Building.11. Transfer Function Models.11.1 Linear Transfer Function Models.11.2 Discrete Dynamic Models Represented by Difference Equations.11.3 Relation Between Discrete and Continuous Models.12. Identification, Fitting, and Checking of Transfer Function Models.12.1 Cross-Correlation Function.12.2 Identification of Transfer Function Models.12.3 Fitting and Checking Transfer Function Models.12.4 Some Examples of Fitting and Checking Transfer Function Models.12.5 Forecasting with Transfer Function Models Using Leading Indicators.12.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions.13. Intervention Analysis Models and Outlier Detection.13.1 Intervention Analysis Methods.13.2 Outlier Analysis for Time Series.13.3 Estimation for ARMA Models with Missing Values.14. Multivariate Time Series Analysis.14.1 Stationary Multivariate Time Series.14.2 Linear Model Representations for Stationary Multivariate Processes.14.3 Nonstationary Vector Autoregressive-Moving Average Models.14.4 Forecasting for Vector Autoregressive-Moving Average Processes.14.5 State-Space Form of the Vector ARMA Models.
Copyright Date
2008
Item description from the seller
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eBay item number:285378346980
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