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Time series analysis for the state-s...

Hagiwara, Junichiro.

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Time series analysis for the state-space model with R/Stan

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time series analysis for the state-space model with R/Stan/ by Junichiro Hagiwara.
Author:	Hagiwara, Junichiro.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xiii, 347 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- 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.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis. -
Online resource:	https://doi.org/10.1007/978-981-16-0711-0
ISBN:	9789811607110

Time series analysis for the state-space model with R/Stan
Hagiwara, Junichiro.

Time series analysis for the state-space model with R/Stan[electronic resource] /by Junichiro Hagiwara. - Singapore :Springer Singapore :2021. - xiii, 347 p. :ill., digital ;24 cm.

Introduction -- 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.

This 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.

ISBN: 9789811607110

Standard No.: 10.1007/978-981-16-0711-0doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280 / .H34 2021

Dewey Class. No.: 519.55

Time series analysis for the state-space model with R/Stan
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245 1 0 $a Time series analysis for the state-space model with R/Stan $h [electronic resource] / $c by Junichiro Hagiwara.
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300 $a xiii, 347 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- 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.
520 $a This 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.
650 0 $a Time-series analysis. $3 532530
650 0 $a State-space methods. $3 643710
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650 2 4 $a Bayesian Inference. $3 3386929
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Econometrics. $3 542934
650 2 4 $a Macroeconomics/Monetary Economics/Financial Economics. $3 2179654
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856 4 0 $u https://doi.org/10.1007/978-981-16-0711-0
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