Spatial analysis of wave direction data using wrapped Gaussian processes
Abstract
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.
Cite
@article{arxiv.1301.1446,
title = {Spatial analysis of wave direction data using wrapped Gaussian processes},
author = {Giovanna Jona-Lasinio and Alan Gelfand and Mattia Jona-Lasinio},
journal= {arXiv preprint arXiv:1301.1446},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.1214/12-AOAS576 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)