東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multivariate time series with linear...

Gomez, Victor.

FindBook

Google Book

Amazon

博客來

Multivariate time series with linear state space structure

紀錄類型:	書目-電子資源 : Monograph/item
正題名/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.
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.
Contained By:	Springer eBooks
標題:	Variate difference method. -
電子資源:	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
LDR:02414nmm a2200313 a 4500 001 2038270
003 DE-He213
005 20161101165309.0
006 m d
007 cr nn 008maaau
008 161209s2016 gw s 0 eng d
020 $a 9783319285993 $q (electronic bk.)
020 $a 9783319285986 $q (paper)
024 7 $a 10.1007/978-3-319-28599-3 $2 doi
035 $a 978-3-319-28599-3
040 $a GP $c GP
041 0 $a eng
050 4 $a HA30.3
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.55 $2 23
090 $a HA30.3 $b .G633 2016
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
650 1 4 $a Statistics. $3 517247
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
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-28599-3
950 $a Mathematics and Statistics (Springer-11649)