Estimation and testing for spatially indexed curves with application to ionospheric and magnetic field trends

Abstract

We develop methodology for the estimation of the functional mean and the functional principal components when the functions form a spatial process. The data consist of curves $X({\mathbf{s}}_k;t),t\in [0,T],$ observed at spatial locations ${\mathbf{s}}_1,{\mathbf{s}}_2,...,{\mathbf{s}}_N$. We propose several methods, and evaluate them by means of a simulation study. Next, we develop a significance test for the correlation of two such functional spatial fields. After validating the finite sample performance of this test by means of a simulation study, we apply it to determine if there is correlation between long-term trends in the so-called critical ionospheric frequency and decadal changes in the direction of the internal magnetic field of the Earth. The test provides conclusive evidence for correlation, thus solving a long-standing space physics conjecture. This conclusion is not apparent if the spatial dependence of the curves is neglected.

Published in at http://dx.doi.org/10.1214/11-AOAS524 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (2)

• Measuring and testing dependence by correlation of distances
• Brownian distance covariance