Optimal tests for the two-sample spherical location problem
type: Dataset
description: 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.
issued: 2026-05-05T11:05:34.757585
language: en
modified: 2026-05-05T11:05:34.757589
contact point: Organization
  fn: CCSD
keyword: 62h11-62h15-62g10
keyword: directional-statistics
keyword: infoeu-reposemanticspreprint
keyword: local-asymptotic-normality
keyword: preprints-working-papers-
keyword: pseudo-fvml-tests
keyword: rank-based-inference
keyword: statapstatistics-statapplications-statap
keyword: statmestatistics-statmethodology-statme
keyword: statthstatistics-statstatistics-theory-statth
keyword: two-sample-spherical-location-problem
landing page: https://lilloa.hal.science/hal-00991919

Optimal tests for the two-sample spherical location problem
type: Distribution
format: HTML
issued: 2026-05-05T11:05:34.764027
modified: 2026-05-05T11:05:34.742701
access URL: https://lilloa.hal.science/hal-00991919

test_moissonnage_selune
type: Agent