{
  "annotation_id": "a7e7370a-6f4a-467f-8404-6fb2408d3261",
  "date_created": "2026-02-17T05:53:15.700000Z",
  "date_modified": "2026-02-17T05:53:15.700000Z",
  "file_hash": "6bf045ac3719da9a505a3cca680ebdad8ef7da1b4bbf1e76d867447bafb85618",
  "private": false,
  "record": {
    "abstract": "We revisit the null distribution of the high-dimensional spatial-sign test of Wang et al. (2015) under mild structural assumptions on the scatter matrix. We show that the standardized test statistic converges to a non-Gaussian limit, characterized as a mixture of a normal component and a weighted chi-square component. To facilitate practical implementation, we propose a wild bootstrap procedure for computing critical values and establish its asymptotic validity. Numerical experiments demonstrate that the proposed bootstrap test delivers accurate size control across a wide range of dependence settings and dimension-sample-size regimes.",
    "arxiv_id": "2601.08736",
    "authors": [
      "Ping Zhao",
      "Long Feng"
    ],
    "categories": [
      "stat.ME"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Note on High Dimensional Spatial-Sign Test for One Sample Problem",
    "url": "https://arxiv.org/abs/2601.08736",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b81d17ba-3ea3-42d8-af7d-121597370f14",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}