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Spatio-temporal circular models with non-separable covariance structure
- Publication Year :
- 2016
- Publisher :
- Springer, 2016.
-
Abstract
- Circular data arise in many areas of application. Recently, there has been interest in looking at circular data collected separately over time and over space. Here, we extend some of this work to the spatio-temporal setting, introducing space-time dependence. We accommodate covariates, implement full kriging and forecasting, and also allow for a nugget which can be time dependent. We work within a Bayesian framework, introducing suitable latent variables to facilitate Markov chain Monte Carlo (MCMC) model fitting. The Bayesian framework enables us to implement full inference, obtaining predictive distributions for kriging and forecasting. We offer comparison between the less flexible but more interpretable wrapped Gaussian process and the more flexible but less interpretable projected Gaussian process. We do this illustratively using both simulated data and data from computer model output for wave directions in the Adriatic Sea off the coast of Italy.
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
010504 meteorology & atmospheric sciences
Computer science
Inference
Latent variable
01 natural sciences
Separable space
Methodology (stat.ME)
010104 statistics & probability
symbols.namesake
Kriging
Covariate
Average prediction error
0101 mathematics
Gaussian process
Statistics - Methodology
0105 earth and related environmental sciences
Continuous ranked probability score
Statistics
Markov chain Monte Carlo
Wrapped distribution
Covariance
Projected distribution
Statistics, Probability and Uncertainty
symbols
Probability and Uncertainty
Algorithm
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....2e57af9b164eb07c06d4f6d87f555bf1