An Introduction To State Space Time Series Analysis

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

Time Series Analysis for the State-Space Model with R/Stan
Author :
Publisher : Springer Nature
Total Pages : 350
Release :
ISBN-10 : 9789811607110
ISBN-13 : 9811607117
Rating : 4/5 (117 Downloads)

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

Download or read book Time Series Analysis for the State-Space Model with R/Stan written by Junichiro Hagiwara and published by Springer Nature. This book was released on 2021-08-30 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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Time Series Analysis for the State-Space Model with R/Stan
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Type: BOOK - Published: 2021-08-30 - Publisher: Springer Nature

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This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time s
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The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulati
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Pages: 398
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A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.