Arxiv:1702.00823V1 [Stat.OT] 2 Feb 2017
Nonparametric Spherical Regression Using Diffeomorphic Mappings M. Rosenthala, W. Wub, E. Klassen,c, Anuj Srivastavab aNaval Surface Warfare Center, Panama City Division - X23, 110 Vernon Avenue, Panama City, FL 32407-7001 bDepartment of Statistics, Florida State University, Tallahassee, FL 32306 cDepartment of Mathematics, Florida State University, Tallahassee, FL 32306 Abstract Spherical regression explores relationships between variables on spherical domains. We develop a nonparametric model that uses a diffeomorphic map from a sphere to itself. The restriction of this mapping to diffeomorphisms is natural in several settings. The model is estimated in a penalized maximum-likelihood framework using gradient-based optimization. Towards that goal, we specify a first-order roughness penalty using the Jacobian of diffeomorphisms. We compare the prediction performance of the proposed model with state-of-the-art methods using simulated and real data involving cloud deformations, wind directions, and vector-cardiograms. This model is found to outperform others in capturing relationships between spherical variables. Keywords: Nonlinear; Nonparametric; Riemannian Geometry; Spherical Regression. 1. Introduction Spherical data arises naturally in a variety of settings. For instance, a random vector with unit norm constraint is naturally studied as a point on a unit sphere. The statistical analysis of such random variables was pioneered by Mardia and colleagues (1972; 2000), in the context of directional data. Common application areas where such data originates include geology, gaming, meteorology, computer vision, and bioinformatics. Examples from geographical domains include plate tectonics (McKenzie, 1957; Chang, 1986), animal migrations, and tracking of weather for- mations. As mobile devices become increasingly advanced and prevalent, an abundance of new spherical data is being collected in the form of geographical coordinates.
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