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

Medium: Buch
ISBN: 978-981-16-0713-4
Verlag: Springer Nature Singapore
Erscheinungstermin: 01.09.2022
Lieferfrist: bis zu 10 Tage

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.  


Produkteigenschaften


  • Artikelnummer: 9789811607134
  • Medium: Buch
  • ISBN: 978-981-16-0713-4
  • Verlag: Springer Nature Singapore
  • Erscheinungstermin: 01.09.2022
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2021
  • Produktform: Kartoniert
  • Gewicht: 552 g
  • Seiten: 347
  • Format (B x H x T): 155 x 235 x 20 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.