Multivariate Time Series With Linear State Space Structure

By: Victor Gomez

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Paperback | 26 May 2018

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Paperback
560 Pages

Dimensions(cm)
23.5 x 15.5 x 2.9

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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 intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.

Industry Reviews

"The book under review is a mathematically solid and comprehensive text, covering in detail the main ingredients of linear estimation theory in state space models. Its emphasis is on the state estimation problems, rather than on statistical inference of the unknown parameters of the model, and from this point of view its scope and spirit is closer to the engineering literature, and to the standard reference ... ." (Pavel Chigansky, Mathematical Reviews, May, 2017)

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