Multivariate Time Series With Linear State Space Structure
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
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 relationship between VARMA and state space models and between Wiener-Kolmogorov theory and Kalman filtering
Autor*in
Víctor Gómez
Themen in »Multivariate Time Series With Linear State Space Structure«
37M10, 62-XX, 62M10, 93E11, 62M20, 60Gxx, 65Fxx time series state space models signal extraction Kalman filter forecasting smoothing multivariate time series algorithms for state space models MATLAB Wiener-Kolmogorov theory
Stimmen zu »Multivariate Time Series With Linear State Space Structure«
“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)
()