| 書目名稱 | Multivariate Time Series With Linear State Space Structure | | 編輯 | Víctor Gómez | | 視頻video | http://file.papertrans.cn/642/641344/641344.mp4 | | 概述 | Provides a comprehensive account of both theory and algorithms for time series and linear state space models.Refers to a webpage with algorithms programmed in MATLAB and numerous examples.Studies the | | 圖書封面 |  | | 描述 | .This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. ?In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intendedfor researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.. | | 出版日期 | Book 2016 | | 關(guān)鍵詞 | 37M10, 62-XX, 62M10, 93E11, 62M20, 60Gxx, 65Fxx; time series; state space models; signal extraction; Kal | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-28599-3 | | isbn_softcover | 978-3-319-80385-2 | | isbn_ebook | 978-3-319-28599-3 | | copyright | Springer International Publishing Switzerland 2016 |
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