Smoothing-based tests with directional random variables

TL;DR

This paper reviews smoothing-based testing procedures for density and regression models involving directional variables on the hypersphere, emphasizing the importance of resampling methods for accurate test calibration.

Contribution

It extends existing smoothing-based tests to directional data, providing asymptotic distributions and highlighting resampling techniques for better calibration.

Findings

Asymptotic distributions derived for directional data tests

Resampling methods improve test calibration

Numerical illustrations demonstrate effectiveness

Abstract

Testing procedures for assessing specific parametric model forms, or for checking the plausibility of simplifying assumptions, play a central role in the mathematical treatment of the uncertain. No certain answers are obtained by testing methods, but at least the uncertainty of these answers is properly quantified. This is the case for tests designed on the two most general data generating mechanisms in practice: distribution/density and regression models. Testing proposals are usually formulated on the Euclidean space, but important challenges arise in non-Euclidean settings, such as when directional variables (i.e., random vectors on the hypersphere) are involved. This work reviews some of the smoothing-based testing procedures for density and regression models that comprise directional variables. The asymptotic distributions of the revised proposals are presented, jointly with some numerical illustrations justifying the need of employing resampling mechanisms for effective test calibration.