03858nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137040002500172050001400197072001600211072002300227072001500250082001400265100008000279245011000359264007500469300003500544336002600579337002600605338003600631347002400667505034200691520114801033650002902181650003802210650001602248650001802264650010702282650012102389650012002510650016002630650008902790650014102879710003403020773002003054776003603074776003603110776003603146856004603182912001403228942000703242950004803249999001903297978-3-319-28599-3DE-He21320181204134229.0cr nn 008mamaa160509s2016 gw | s |||| 0|eng d a97833192859939978-3-319-28599-37 a10.1007/978-3-319-28599-32doi aISI Library, Kolkata 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh 7aPBT2thema04a519.52231 aGómez, Víctor.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aMultivariate Time Series With Linear State Space Structureh[electronic resource] /cby Víctor Gómez. 1aCham :bSpringer International Publishing :bImprint: Springer,c2016. aXVII, 541 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda0 aPreface -- 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. aThis 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. 0aMathematical statistics. 0aDistribution (Probability theory. 0aStatistics. 0aEconometrics.14aStatistical Theory and Methods.0http://scigraph.springernature.com/things/product-market-codes/S1100124aStatistics and Computing/Statistics Programs.0http://scigraph.springernature.com/things/product-market-codes/S1200824aProbability Theory and Stochastic Processes.0http://scigraph.springernature.com/things/product-market-codes/M2700424aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.0http://scigraph.springernature.com/things/product-market-codes/S1702024aEconometrics.0http://scigraph.springernature.com/things/product-market-codes/W2901024aStatistics for Business/Economics/Mathematical Finance/Insurance.0http://scigraph.springernature.com/things/product-market-codes/S170102 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z978331928598608iPrinted edition:z978331928600608iPrinted edition:z978331980385240uhttps://doi.org/10.1007/978-3-319-28599-3 aZDB-2-SMA cEB aMathematics and Statistics (Springer-11649) c426565d426565