Dynamic Time Series Models using R-INLA
An Applied Perspective
1st Edition
Published on 10. August 2022
296 pages
978-1-000-62260-7 (ISBN)
Description
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.
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.
Reviews / Votes
"This book will interest current R-users with a background in time series analyses who would like to expand their knowledge regarding INLA and its application with R-INLA package. This book also provides illustrative examples which can contribute to the understanding of the applications of these methods. This book can also benefit academic researchers who would like to apply these types of approaches in their fields."Sebastien Bailly, French National Center for Medical Research (INSERM), France, ISCB, May 2023
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
17 Tables, black and white; 68 Line drawings, color; 20 Line drawings, black and white; 68 Illustrations, color; 20 Illustrations, black and white
ISBN-13
978-1-000-62260-7 (9781000622607)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nalini Ravishanker | Balaji Raman | Refik Soyer
Dynamic Time Series Models using R-INLA
An Applied Perspective
Book
08/2022
1st Edition
Chapman & Hall/CRC
€153.10
Shipment within 15-20 days
Persons
Nalini Ravishanker is a professor in the Department of Statistics at the University of Connecticut, Storrs, USA.
Balaji Raman is a statistician at Cogitaas AVA, Mumbai, India.
Refik Soyer is a professor in the Department of Decision Sciences at The George Washington University, Washington D.C., USA.
Balaji Raman is a statistician at Cogitaas AVA, Mumbai, India.
Refik Soyer is a professor in the Department of Decision Sciences at The George Washington University, Washington D.C., USA.
Content
Preface. 1. Bayesian Analysis. 2. A Review of INLA. 3. Modeling Univariate Time Series. 4. More Topics on DLMs with R-INLA. 5. Modeling Time Series with Exogenous Predictors. 6. Structural Time Series Decomposition using R-INLA. 7. Hierarchical DLM. 8. INLA for Multivariate Dynamic Models. 9. Modeling Binary Time Series. 10. Modeling Count Time Series. 11. Modeling Stochastic Volatility. 12. Comparison of R-INLA to Other Bayesian Alternatives. 13. Resources for the User.
