Dynamic time series models using R-INLA an applied perspective / Nalini Ravishanker, Balaji Raman and Refik Soyer.

Author/creator Ravishanker, Nalini
Other author Raman, Balaji.
Other author Soyer, Refik.
Format Electronic
EditionFirst edition.
Publication InfoBoca Raton : CRC Press, 2022.
Descriptionpages cm
Supplemental ContentFull text available from Taylor & Francis eBooks
Subjects

Contents Bayesian analysis -- A review of INLA -- Modeling univariate time series -- More topics on DLMs with R-INLA -- Modeling time series with exogenous predictors -- Structural time series decomposition using R-INLA -- Hierarchical DLM -- INLA for multivariate dynamic models -- Modeling binary time series -- Modeling count time series -- Modeling stochastic volatility -- Comparison of R-INLA to other Bayesian alternatives -- Resources for the user.
Abstract "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"-- Provided by publisher.
Bibliography noteIncludes bibliographical references and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2022004440
ISBN9780367654276 (hardback)
ISBN9780367680626 (paperback)
ISBN(ebook)

Availability

Library Location Call Number Status Item Actions
Electronic Resources Access Content Online ✔ Available