Nalini Ravishanker, Balaji Raman, Refik Soyer

Dynamic Time Series Models using R-INLA (eBook, ePUB)

An Applied Perspective

• Format: ePub

Produktbeschreibung

This Book 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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 296
• Erscheinungstermin: 10. August 2022
• Englisch
• ISBN-13: 9781000622874
• Artikelnr.: 64308962

Autorenporträt

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.

Inhaltsangabe

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.