Testing uniformity on high-dimensional spheres: The non-null behaviour of the Bingham test

Testing uniformity on the unit sphere of R is a fundamental problem in directional statistics. In the framework of axial data, the most classical test of uniformity is the Bingham [8] test. Remarkably, this test does not need any modification to meet asymptotically the target null size in high-dimensional scenarios where p = pn diverges to infinity with the sample size n. However, while the non-null asymptotic behaviour of the Bingham test is well understood in standard asymptotic scenarios where n diverges to infinity with p fixed, nothing is known on the power of this test in high dimensions, not even under standard parametric alternatives such as Watson distributions. In this work, we therefore study the non-null behaviour of the Bingham test in high dimensions. First, we consider a semiparametric class of alternatives that includes Watson alternatives and we derive a local asymptotic normality (LAN) property. An application of Le Cam’s third lemma reveals that the Bingham test is blind to the corresponding contiguous alternatives, though. By using martingale central limit theorems, we therefore study the non-null behaviour of the Bingham test under more severe alternatives. Far from restricting to the aforementioned semiparametric alternatives, our results cover a broad class of rotationally symmetric alternatives, which allows us to consider non-axial alternatives, too. In every distributional framework we consider, the “detection threshold” of the Bingham test is identified and a comparison with the classical test of uniformity for non-axial data, namely the Rayleigh [40] test, is made possible. In the framework of axial data, we derive a lower bound on the minimax separation rate and establish that the Bingham test is minimax rate-optimal in the class of Watson distributions. MSC 2010 subject classifications: Primary 62H11, 62F05; secondary 62E20.