
eBook - ePub
Dynamic Time Series Models using R-INLA
An Applied Perspective
- 282 pages
- English
- ePUB (mobile friendly)
- 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
Table of contents
- Cover Page
- Half-Title Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Preface
- 1 Bayesian Analysis
- 2 A Review of INLA
- 3 Details of R-INLA for Time Series
- 4 Modeling Univariate Time Series
- 5 Time Series Regression Models
- 6 Hierarchical Dynamic Models for Panel Time Series
- 7 Non-Gaussian Continuous Responses
- 8 Modeling Categorical Time Series
- 9 Modeling Count Time Series
- 10 Modeling Stochastic Volatility
- 11 Spatio-temporal Modeling
- 12 Multivariate Gaussian Dynamic Modeling
- 13 Hierarchical Multivariate Time Series
- 14 Resources for the User
- Bibliography
- Index