{
  "annotation_id": "1dcb073b-e944-42af-bd1a-0f0f0a0a5808",
  "date_created": "2026-02-17T05:53:19.953000Z",
  "date_modified": "2026-02-17T05:53:19.953000Z",
  "file_hash": "d6fba5c9967ebe5658233e08416dac38f7d4e7908627ba500fcae47448106ba2",
  "private": false,
  "record": {
    "abstract": "In Astronomy, Survival Analysis and Epidemiology, among many other fields, doubly truncated data often appear. Double truncation generally induces a sampling bias, so ordinary estimators may be inconsistent. In this paper, smoothing spline density estimation from doubly truncated data is investigated. For this purpose, an appropriate correction of the penalized likelihood that accounts for the sampling bias is considered. The theoretical properties of the estimator are discussed, and its practical performance is evaluated through simulations. Two real datasets are analyzed using the proposed method for illustrative purposes. Comparison to kernel density smoothing is included.",
    "arxiv_id": "2601.09576",
    "authors": [
      "David Bamio",
      "Jacobo de U\u00f1a-\u00c1lvarez"
    ],
    "categories": [
      "stat.ME"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Smoothing spline density estimation from doubly truncated data",
    "url": "https://arxiv.org/abs/2601.09576",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b6a707a1-4fd8-45ff-bc8a-358bed425b25",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}