Work

Spatial analysis of wave direction data using wrapped Gaussian processes

Year: 2012

Type: article

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.

Source: The Annals of Applied Statistics

DOI: 10.1214/12-aoas576

Authors: Giovanna Jona-Lasinio, Alan Gelfand, Mattia Jona-Lasinio

Institutions: Sapienza University of Rome, Duke University, Leibniz University Hannover

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: Collaborative Research for Developing ATD: Bayesian Methods in Syndromic Surveillance: CAR Models and Computational Implementation, CDI-Type II: Integrating Algorithmic and Stochastic Modeling Techniques for Environmental Prediction