We tackle the classical two-sample spherical location problem for directional data by having recourse to the Le Cam methodology, habitually used in classical \linear" multivariate analysis. More precisely we construct locally and asymptotically optimal (in the maximin sense) parametric tests, which we then turn into semi-parametric ones in two distinct ways. First, by using a studentization argument; this leads to so-called pseudo-FvML tests. Second, by resorting to the invariance principle; this leads to e cient rank-based tests. Within each construction, the semi-parametric tests inherit optimality under a given distribution (the FvML in the rst case, any rotationally symmetric one in the second) from their parametric counterparts and also improve on the latter by being valid under the whole class of rotationally symmetric distributions. Asymptotic relative e ciencies are calculated and the nite-sample behavior of the proposed tests is investigated by means of a Monte Carlo simulation.
Optimal tests for the two-sample spherical location problem