Multivariate Time Series With Linear State Space Structure (Paperback, Softcover reprint of the original 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	May 2018
First published:	2016
Authors:	Victor Gomez
Dimensions:	235 x 155 x 29mm (L x W x T)
Format:	Paperback
Pages:	541
Edition:	Softcover reprint of the original 1st ed. 2016
ISBN-13:	978-3-319-80385-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Economics > Econometrics > Economic statistics Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Promotions
LSN:	3-319-80385-9
Barcode:	9783319803852

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