{
  "annotation_id": "baf06c56-b8ba-4c59-9b55-85043e2f858c",
  "date_created": "2026-03-02T18:00:42.345000Z",
  "date_modified": "2026-03-02T18:00:42.345000Z",
  "file_hash": "d3ecb379e2b2deaa3b66d10ec647853cd91b7fa64ffe7da9d58ecb031af9ef4d",
  "private": false,
  "record": {
    "abstract": "The estimation of signal frequency count in the presence of background noise\nhas had much discussion in the recent physics literature, and Mandelkern [1]\nbrings the central issues to the statistical community, leading in turn to\nextensive discussion by statisticians. The primary focus however in [1] and the\naccompanying discussion is on the construction of a confidence interval. We\nargue that the likelihood function and $p$-value function provide a\ncomprehensive presentation of the information available from the model and the\ndata. This is illustrated for Gaussian and Poisson models with lower bounds for\nthe mean parameter.",
    "arxiv_id": "physics/0303111",
    "authors": [
      "D. A. S. Fraser",
      "N. Reid",
      "A. C. M. Wong"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1103/PhysRevD.69.033002",
    "title": "Inference for bounded parameters",
    "url": "https://arxiv.org/abs/physics/0303111"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "82fd951a-24eb-499b-bafe-2ce3ce02de6b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}