Open Access
December 2012 Spatial analysis of wave direction data using wrapped Gaussian processes
Giovanna Jona-Lasinio, Alan Gelfand, Mattia Jona-Lasinio
Ann. Appl. Stat. 6(4): 1478-1498 (December 2012). DOI: 10.1214/12-AOAS576


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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Giovanna Jona-Lasinio. Alan Gelfand. Mattia Jona-Lasinio. "Spatial analysis of wave direction data using wrapped Gaussian processes." Ann. Appl. Stat. 6 (4) 1478 - 1498, December 2012.


Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62094
MathSciNet: MR3058672
Digital Object Identifier: 10.1214/12-AOAS576

Keywords: Bayesian kriging , Gaussian processes , hierarchical model , latent variables

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 4 • December 2012
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