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Hardcover - 9789811607103
160,49 €
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Beschreibung
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.
Details
| Verlag | Springer Singapore |
| Ersterscheinung | 01. September 2022 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 552 Gramm |
| Format | Softcover |
| ISBN-13 | 9789811607134 |
| Seiten | 347 |
Schlagwörter
Applied Statistics, Bayesian Inference, Bayesianische Inferenz, Macroeconomics and Monetary Economics, Makroökonomie, Mathematische und statistische Software, Quantitative Economics, Statistical Theory and Methods, Statistics and Computing, Wahrscheinlichkeitsrechnung und Statistik, Wirtschaftstheorie und -philosophie
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