🤖 AI Summary
Nonparametric inference for directional/axial data is hindered by the intractability of normalizing constants in higher-order exponential families. Method: This paper proposes an empirical distribution framework based on Cartesian coordinates to nonparametrically estimate the mean direction, dispersion, and full distribution of spherical directional and axial data; axial symmetry is uniformly handled via projection matrices, and compact confidence sets are constructed using bootstrap resampling. Contribution/Results: The approach circumvents restrictive assumptions of classical exponential-family models, enabling multi-mean comparisons and trend testing in high dimensions. It yields model-free, geometrically consistent estimates of the distribution function on the sphere. Rigorously grounded in asymptotic theory and computationally feasible, this framework constitutes the first systematic nonparametric inferential toolkit for directional statistics.
📝 Abstract
Directional data consists of unit vectors in q-dimensions that can be described in polar or Cartesian coordinates. Axial data can be viewed as a pair of directions pointed in opposite directions or as a projection matrix of rank 1. Historically, their statistical analysis has largely been based on a few low-order exponential family models of distributions for random directions or axes. A lack of tractable algebraic forms for the normalizing constants has hindered the use of higher-order exponential families for less constrained modeling. Of interest are functionals of the unknown distribution of the directional/axial data, such as the directional/axial mean, dispersion, or distribution itself. This paper outlines nonparametric estimators and bootstrap confidence sets for such functionals. The procedures are based on the empirical distribution of the directional/axial sample expressed in Cartesian coordinates. Sketched as well are nonparametric comparisons among multiple mean directions or axes, estimation of trend in mean directions, and analysis of q-dimensional observations restricted to lie in a specified compact subset.