Inference on the mode of weak directional signals: a Le Cam perspective on hypothesis testing near singularities

TL;DR

This paper analyzes the difficulty of testing the mode of weak directional signals near singularities, revealing how classical tests behave under diminishing signal strength and establishing the optimality of the Watson test.

Contribution

It provides a Le Cam theoretical framework for understanding hypothesis testing with weak signals and demonstrates the adaptive optimality of the Watson test in this context.

Findings

Classical tests behave differently as signal weakens.

The limiting experiments depend on the signal strength rate.

The Watson test is adaptively rate-consistent and optimal.

Abstract

We revisit, in an original and challenging perspective, the problem of testing the null hypothesis that the mode of a directional signal is equal to a given value. Motivated by a real data example where the signal is weak, we consider this problem under asymptotic scenarios for which the signal strength goes to zero at an arbitrary rate~$\eta_n$. Both under the null and the alternative, we focus on rotationally symmetric distributions. We show that, while they are asymptotically equivalent under fixed signal strength, the classical Wald and Watson tests exhibit very different (null and non-null) behaviours when the signal becomes arbitrarily weak. To fully characterize how challenging the problem is as a function of~$\eta_n$, we adopt a Le Cam, convergence-of-statistical-experiments, point of view and show that the resulting limiting experiments crucially depend on~$\eta_n$. In the light of these results, the Watson test is shown to be \emph{adaptively} rate-consistent and essentially adaptively Le Cam optimal. Throughout, our theoretical findings are illustrated via Monte-Carlo simulations. The practical relevance of our results is also shown on the real data example that motivated the present work.