Modeling spatial anisotropy via regression with partial di fferential regularization

Keywords

Statistical learning
Code:
45/2018
Title:
Modeling spatial anisotropy via regression with partial di fferential regularization
Date:
Monday 13th August 2018
Author(s):
Bernardi, M.S.; Carey, M.; Ramsay, J.O.; Sangalli, L.M.
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Abstract:
We consider the problem of analyzing spatially distributed data characterized by spatial anisotropy. Following a functional data analysis approach, we propose a method based on regression with partial di fferential regularization, where the diff erential operator in the regularizing term is anisotropic and is derived from data. We show that the method correctly identifi es the direction and intensity of anisotropy and returns an accurate estimate of the spatial eld. The method compares favorably to both isotropic and anisotropic kriging, as tested in simulation studies under various scenarios. The method is then applied to the analysis of Switzerland rainfall data.
This report, or a modified version of it, has been also submitted to, or published on
Journal of Multivariate Analysis, 2018, 167, 15-30.