Time series analysis for the state-space model with R/Stan /
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| Author / Creator: | Hagiwara, Junichiro, author. |
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| Imprint: | Singapore : Springer, [2021] ©2021 |
| Description: | 1 online resource (350 pages) : illustrations |
| Language: | English |
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| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/12631731 |
| 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. <br> <br> |
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| Physical Description: | 1 online resource (350 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9789811607110 9811607117 9789811607103 9811607109 |