Time Series Analysis for the State-Space Model with R/Stan
Título:
Time Series Analysis for the State-Space Model with R/Stan
ISBN:
9789811607110
Autor personal:
Edición:
1st ed. 2021.
PRODUCTION_INFO:
Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
Descripción física:
XIII, 347 p. 216 illus. online resource.
Contenido:
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.
Síntesis:
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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Idioma:
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