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Dynamic Time Series Models using R-INLA 1st Edition 2022 Hardbound at Meripustak

Dynamic Time Series Models using R-INLA 1st Edition 2022 Hardbound

Books from same Author: Ravishanker, Nalini

Books from same Publisher: Taylor and Francis Ltd

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    • General Information  
    Author(s)Ravishanker, Nalini
    PublisherTaylor and Francis Ltd
    Edition1st Edition
    ISBN9780367654276
    Pages282
    BindingHardbound
    LanguageEnglish
    Publish YearAugust 2022

    Description

    Taylor and Francis Ltd Dynamic Time Series Models using R-INLA 1st Edition 2022 Hardbound by Ravishanker, Nalini

    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. 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.