LEADER 04166nam  22006135  450 
001 9910254061203321
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010   $a3-319-28599-8
024 7 $a10.1007/978-3-319-28599-3
035   $a(CKB)3710000000657952
035   $a(EBL)4526239
035   $a(DE-He213)978-3-319-28599-3
035   $a(MiAaPQ)EBC4526239
035   $a(PPN)194078779
035   $a(EXLCZ)993710000000657952
100   $a20160509d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMultivariate time series with linear state space structure /$fby Víctor Gómez
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (553 p.)
300   $aDescription based upon print version of record.
311   $a3-319-28598-X 
320   $aIncludes bibliographical references and indexes.
327   $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.
330   $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.
606   $aStatistics 
606   $aProbabilities
606   $aEconometrics
606   $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001
606   $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020
606   $aEconometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/W29010
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
615  0$aStatistics .
615  0$aProbabilities.
615  0$aEconometrics.
615 14$aStatistical Theory and Methods.
615 24$aStatistics and Computing/Statistics Programs.
615 24$aProbability Theory and Stochastic Processes.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aEconometrics.
615 24$aStatistics for Business, Management, Economics, Finance, Insurance.
676   $a519.5
700   $aGómez$b Víctor$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755989
906   $aBOOK
912   $a9910254061203321
996   $aMultivariate time series with linear state space structure$91523479
997   $aUNINA