TY - BOOK
AU - Gómez,Víctor
ED - SpringerLink (Online service)
TI - Multivariate Time Series With Linear State Space Structure
SN - 9783319285993
AV - QA276-280
U1 - 519.5 23
PY - 2016///
CY - Cham
PB - Springer International Publishing, Imprint: Springer
KW - Mathematical statistics
KW - Distribution (Probability theory
KW - Statistics
KW - Econometrics
KW - Statistical Theory and Methods
KW - Statistics and Computing/Statistics Programs
KW - Probability Theory and Stochastic Processes
KW - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
KW - Statistics for Business/Economics/Mathematical Finance/Insurance
N1 - 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
N2 - 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
UR - https://doi.org/10.1007/978-3-319-28599-3
ER -