A reparametrization approach for dynamic space-time models
From MaRDI portal
Publication:2324055
DOI10.1080/15598608.2008.10411856zbMath1427.62113OpenAlexW2036442742WikidataQ41871976 ScholiaQ41871976MaRDI QIDQ2324055
Sujit Kumar Ghosh, Hyeyoung Lee
Publication date: 13 September 2019
Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3095523
Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)
Related Items
Cites Work
- Unnamed Item
- Bayesian forecasting and dynamic models.
- Estimation of parameterized spatio-temporal dynamic models
- The kriged Kalman filter. (With discussion)
- Dynamic Models for Spatiotemporal Data
- Random Effects Selection in Linear Mixed Models
- A Spatiotemporal Model for Mexico City Ozone Levels
- Venezuelan Rainfall Data Analysed by Using a Bayesian Space–time Model
- Bayesian analysis of covariance matrices and dynamic models for longitudinal data
- Nonseparable, Stationary Covariance Functions for Space–Time Data
- Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
- Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
- A dimension-reduced approach to space-time Kalman filtering
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
- Space–Time Covariance Functions
- Geostatistical space-time models: a review