Detecting the direction of a signal on high-dimensional spheres: non-null and Le Cam optimality results

Abstract

We consider one of the most important problems in directional statistics, namely the problem of testing the null hypothesis that the spike direction \({\pmb \theta }\) of a Fisher–von Mises–Langevin distribution on the p-dimensional unit hypersphere is equal to a given direction \({\pmb \theta }_0\). After a reduction through invariance arguments, we derive local asymptotic normality (LAN) results in a general high-dimensional framework where the dimension \(p_n\) goes to infinity at an arbitrary rate with the sample size n, and where the concentration \(\kappa _n\) behaves in a completely free way with n, which offers a spectrum of problems ranging from arbitrarily easy to arbitrarily challenging ones. We identify various asymptotic regimes, depending on the convergence/divergence properties of \((\kappa _n)\), that yield different contiguity rates and different limiting experiments. In each regime, we derive Le Cam optimal tests under specified \(\kappa _n\) and we compute, from the Le Cam third lemma, asymptotic powers of the classical Watson test under contiguous alternatives. We further establish LAN results with respect to both spike direction and concentration, which allows us to discuss optimality also under unspecified \(\kappa _n\). To investigate the non-null behavior of the Watson test outside the parametric framework above, we derive its local asymptotic powers through martingale CLTs in the broader, semiparametric, model of rotationally symmetric distributions. A Monte Carlo study shows that the finite-sample behaviors of the various tests remarkably agree with our asymptotic results.