Dynamic Time Series Models using R-INLA
eBook - ePub

Dynamic Time Series Models using R-INLA

An Applied Perspective

  1. 282 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Dynamic Time Series Models using R-INLA

An Applied Perspective

About this book

Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework.

The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series.

Key Features:

  • Introduction and overview of R-INLA for time series analysis.
  • Gaussian and non-Gaussian state space models for time series.
  • State space models for time series with exogenous predictors.
  • Hierarchical models for a potentially large set of time series.
  • Dynamic modelling of stochastic volatility and spatio-temporal dependence.

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Yes, you can access Dynamic Time Series Models using R-INLA by Nalini Ravishanker,Balaji Raman,Refik Soyer in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2022
Print ISBN
9780367654276
eBook ISBN
9781000622874

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Dedication Page
  6. Contents
  7. Preface
  8. 1 Bayesian Analysis
  9. 2 A Review of INLA
  10. 3 Details of R-INLA for Time Series
  11. 4 Modeling Univariate Time Series
  12. 5 Time Series Regression Models
  13. 6 Hierarchical Dynamic Models for Panel Time Series
  14. 7 Non-Gaussian Continuous Responses
  15. 8 Modeling Categorical Time Series
  16. 9 Modeling Count Time Series
  17. 10 Modeling Stochastic Volatility
  18. 11 Spatio-temporal Modeling
  19. 12 Multivariate Gaussian Dynamic Modeling
  20. 13 Hierarchical Multivariate Time Series
  21. 14 Resources for the User
  22. Bibliography
  23. Index