| 書目名稱 | Elements of Multivariate Time Series Analysis | | 編輯 | Gregory C. Reinsel | | 視頻video | http://file.papertrans.cn/308/307610/307610.mp4 | | 叢書名稱 | Springer Series in Statistics | | 圖書封面 |  | | 描述 | The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto- correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time | | 出版日期 | Textbook 19931st edition | | 關(guān)鍵詞 | Likelihood; Radiologieinformationssystem; correlation; economics; forecasting; integration; time series an | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4684-0198-1 | | isbn_ebook | 978-1-4684-0198-1Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer-Verlag New York, Inc. 1993 |
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