期刊
ANNALS OF APPLIED STATISTICS
卷 6, 期 4, 页码 1478-1498出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-AOAS576
关键词
Bayesian kriging; Gaussian processes; hierarchical model; latent variables
资金
- NSF [DMS-09-14906, CDI 0940671]
Directional data arise in various contexts such as oceanography (wave directions) and meteorology (wind directions), as well as with measurements on a periodic scale (weekdays, hours, etc.). Our contribution is to introduce a model-based approach to handle periodic data in the case of measurements taken at spatial locations, anticipating structured dependence between these measurements. We formulate a wrapped Gaussian spatial process model for this setting, induced from a customary linear Gaussian process. We build a hierarchical model to handle this situation and show that the fitting of such a model is possible using standard Markov chain Monte Carlo methods. Our approach enables spatial interpolation (and can accommodate measurement error). We illustrate with a set of wave direction data from the Adriatic coast of Italy, generated through a complex computer model.
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