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Multivariate time series with linear...

Gomez, Victor.

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Multivariate time series with linear state space structure

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multivariate time series with linear state space structure/ by Victor Gomez.
Author:	Gomez, Victor.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xvii, 541 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Variate difference method. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-28599-3
ISBN:	9783319285993

Multivariate time series with linear state space structure
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:

2195355
Variate difference method.

LC Class. No.: HA30.3

Dewey Class. No.: 519.55

Multivariate time series with linear state space structure
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100 1 $a Gomez, Victor. $3 2195354
245 1 0 $a Multivariate time series with linear state space structure $h [electronic resource] / $c by Victor Gomez.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvii, 541 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
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.
650 0 $a Variate difference method. $3 2195355
650 0 $a Orthographic projection. $3 2195356
650 0 $a Linear models (Statistics) $3 533190
650 0 $a Linear time invariant systems. $3 660801
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650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Statistics and Computing/Statistics Programs. $3 894293
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
650 2 4 $a Econometrics. $3 542934
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 891081
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-28599-3
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