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＜電子ブック＞
Time Series Analysis for the State-Space Model with R/Stan / by Junichiro Hagiwara

版	1st ed. 2021.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2021
大きさ	XIII, 347 p. 216 illus : online resource
著者標目	*Hagiwara, Junichiro author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Mathematical statistics—Data processing LCSH:Econometrics LCSH:Macroeconomics FREE:Applied Statistics FREE:Statistics and Computing FREE:Bayesian Inference FREE:Statistical Theory and Methods FREE:Quantitative Economics FREE:Macroeconomics and Monetary Economics
一般注記	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. HTTP:URL=https://doi.org/10.1007/978-981-16-0711-0

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