LEADER 03503nam  22006975  450 
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010   $a9789811607110
010   $a9811607117
024 7 $a10.1007/978-981-16-0711-0
035   $a(CKB)5600000000003516
035   $a(MiAaPQ)EBC6715913
035   $a(Au-PeEL)EBL6715913
035   $a(PPN)257355294
035   $a(DE-He213)978-981-16-0711-0
035   $a(EXLCZ)995600000000003516
100   $a20210830d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aTime Series Analysis for the State-Space Model with R/Stan /$fby Junichiro Hagiwara
205   $a1st ed. 2021.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021.
215   $a1 online resource (350 pages)
311 08$a9789811607103 
311 08$a9811607109 
327   $aIntroduction -- Fundamental of probability and statistics -- Fundamentals of handling time series data with R -- Quick tour of time series analysis -- State-space model -- State estimation in the state-space model -- Batch solution for linear Gaussian state-space model -- Sequential solution for linear Gaussian state-space model -- Introduction and analysis examples of a well-known component model -- Batch solution for general state-space model -- Sequential solution for general state-space model -- Example of applied analysis in general state-space model.
330   $aThis book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader?s analytical capability. .
606   $aStatistics
606   $aMathematical statistics$xData processing
606   $aStatistics
606   $aEconometrics
606   $aMacroeconomics
606   $aApplied Statistics
606   $aStatistics and Computing
606   $aBayesian Inference
606   $aStatistical Theory and Methods
606   $aQuantitative Economics
606   $aMacroeconomics and Monetary Economics
615  0$aStatistics.
615  0$aMathematical statistics$xData processing.
615  0$aStatistics.
615  0$aEconometrics.
615  0$aMacroeconomics.
615 14$aApplied Statistics.
615 24$aStatistics and Computing.
615 24$aBayesian Inference.
615 24$aStatistical Theory and Methods.
615 24$aQuantitative Economics.
615 24$aMacroeconomics and Monetary Economics.
676   $a519.55
700   $aHagiwara$b Junichiro$0908140
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910495161903321
996   $aTime Series Analysis for the State-Space Model with R$92031341
997   $aUNINA