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Multivariate time series with linear state space structure[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.55
書名/作者:	Multivariate time series with linear state space structure/ by Victor Gomez.
作者:	Gomez, Victor.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xvii, 541 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Variate difference method.
標題:	Orthographic projection.
標題:	Linear models (Statistics)
標題:	Linear time invariant systems.
標題:	Statistics.
標題:	Statistical Theory and Methods.
標題:	Statistics and Computing/Statistics Programs.
標題:	Probability Theory and Stochastic Processes.
標題:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
標題:	Econometrics.
標題:	Statistics for Business/Economics/Mathematical Finance/Insurance.
ISBN:	9783319285993
ISBN:	9783319285986
內容註:	Preface -- Computer Software -- Orthogonal Projection -- Linear Models -- Stationarity and Linear Time Series Models -- The State Space Model -- Time Invariant State Space Models -- Time Invariant State Space Models With Inputs -- Wiener-Kolmogorov Filtering and Smoothing -- SSMMATLAB -- Bibliography -- Author Index -- Subject Index.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-28599-3

Multivariate time series with linear state space structure[electronic resource] /
Gomez, Victor.

Multivariate time series with linear state space structure[electronic resource] /by Victor Gomez. - Cham :Springer International Publishing :2016. - xvii, 541 p. :ill., digital ;24 cm.

Preface -- Computer Software -- Orthogonal Projection -- Linear Models -- Stationarity and Linear Time Series Models -- The State Space Model -- Time Invariant State Space Models -- Time Invariant State Space Models With Inputs -- Wiener-Kolmogorov Filtering and Smoothing -- SSMMATLAB -- Bibliography -- Author Index -- Subject Index.

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.

ISBN: 9783319285993

Standard No.: 10.1007/978-3-319-28599-3doiSubjects--Topical Terms:

647239
Variate difference method.

LC Class. No.: HA30.3

Dewey Class. No.: 519.55

Multivariate time series with linear state space structure[electronic resource] /
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520 $a 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.
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650 2 4 $a Probability Theory and Stochastic Processes. $3 463894
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 464764
650 2 4 $a Econometrics. $3 186734
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 464928
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