Time Series Analysis for the State-Space Model with R/Stan / by Junichiro Hagiwara

By:

Hagiwara, Junichiro [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer eBook CollectionPublisher: Singapore : Springer, 2021Description: 1 Online-Ressource (XIII, 347 p. 216 illus.)ISBN:

9789811607110

Subject(s):

Additional physical formats: 9789811607103 | 9789811607127 | 9789811607134 | Erscheint auch als: 9789811607103 Druck-Ausgabe | Erscheint auch als: 9789811607127 Druck-Ausgabe | Erscheint auch als: 9789811607134 Druck-Ausgabe | Erscheint auch als: Time series analysis for the state-space model with R/Stan. Druck-Ausgabe Singapore : Springer Nature, 2021. xiii, 347 SeitenDDC classification:

519.5 23

RVK: RVK: SK 820DOI: DOI: 10.1007/978-981-16-0711-0Online resources:

Summary: 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.Summary: 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. .PPN: PPN: 1769715363Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS

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