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Elements of Multivariate Time Series Analysis, Paperback by Reinsel, Gregory ...
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Item specifics
- Condition
- Book Title
- Elements of Multivariate Time Series Analysis
- ISBN
- 9780387406190
- Subject Area
- Mathematics
- Publication Name
- Elements of Multivariate Time Series Analysis
- Publisher
- Springer New York
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / General, Probability & Statistics / Multivariate Analysis
- Publication Year
- 2003
- Series
- Springer Series in Statistics Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Features
- Revised
- Item Weight
- 24 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xvii, 358 Pages
About this product
Product Identifiers
Publisher
Springer New York
ISBN-10
0387406190
ISBN-13
9780387406190
eBay Product ID (ePID)
6030121
Product Key Features
Number of Pages
Xvii, 358 Pages
Language
English
Publication Name
Elements of Multivariate Time Series Analysis
Subject
Probability & Statistics / General, Probability & Statistics / Multivariate Analysis
Publication Year
2003
Features
Revised
Type
Textbook
Subject Area
Mathematics
Series
Springer Series in Statistics Ser.
Format
Trade Paperback
Dimensions
Item Weight
24 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
Dewey Edition
20
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.55
Edition Description
Revised edition
Synopsis
Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures., In this revised edition, some additional topics have been added to the original version, and certain existing materials have been expanded, in an attempt to pro- vide a more complete coverage of the topics of time-domain multivariate time series modeling and analysis. The most notable new addition is an entirely new chapter that gives accounts on various topics that arise when exogenous vari- ables are involved in the model structures, generally through consideration of the so-called ARMAX models; this includes some consideration of multivariate linear regression models with ARMA noise structure for the errors. Some other new material consists of the inclusion of a new Section 2. 6, which introduces state-space forms of the vector ARMA model at an earlier stage so that readers have some exposure to this important concept much sooner than in the first edi- tion; a new Appendix A2, which provides explicit details concerning the rela- tionships between the autoregressive (AR) and moving average (MA) parameter coefficient matrices and the corresponding covariance matrices of a vector ARMA process, with descriptions of methods to compute the covariance matrices in terms of the AR and MA parameter matrices; a new Section 5., Elements of Multivariate Time Series Analysis introduces the basic concepts and methods that are useful in the analysis and modeling of multivariate time series data that may arise in business and economics, engineering, geophysical sciences, and other fields. The book concentrates on the time-domain analysis of multivariate time series, and assumes a background in univariate time series analysis. The book also includes exercise sets and multivariate time series data sets. In addition to serving as a textbook, this book will also be useful to researchers and graduate students in the areas of statistics, econometrics, business, and engineering., In this revised edition, some additional topics have been added to the original version, and certain existing materials have been expanded, in an attempt to pro vide a more complete coverage of the topics of time-domain multivariate time series modeling and analysis. The most notable new addition is an entirely new chapter that gives accounts on various topics that arise when exogenous vari ables are involved in the model structures, generally through consideration of the so-called ARMAX models; this includes some consideration of multivariate linear regression models with ARMA noise structure for the errors. Some other new material consists of the inclusion of a new Section 2. 6, which introduces state-space forms of the vector ARMA model at an earlier stage so that readers have some exposure to this important concept much sooner than in the first edi tion; a new Appendix A2, which provides explicit details concerning the rela tionships between the autoregressive (AR) and moving average (MA) parameter coefficient matrices and the corresponding covariance matrices of a vector ARMA process, with descriptions of methods to compute the covariance matrices in terms of the AR and MA parameter matrices; a new Section 5.
LC Classification Number
QA276-280
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