1st Edition
Dynamic Time Series Models using R-INLA An Applied Perspective
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.
Biography
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.
"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."
Sébastien Bailly, French National Center for Medical Research (INSERM), France, ISCB, May 2023
