{
  "annotation_id": "44b786ec-42b1-4181-bf68-cdcf5f237a53",
  "date_created": "2026-02-17T05:53:12.074000Z",
  "date_modified": "2026-02-17T05:53:12.074000Z",
  "file_hash": "13bdcfa59cd2eb2b41a4ff8fc77ca6a4657ec058869d0cc0c7d00814516cbba4",
  "private": false,
  "record": {
    "abstract": "Penalised estimation methods for point processes usually rely on a large amount of independent repetitions for cross-validation purposes. However, in the case of a single realisation of the process, existing cross-validation methods may be impractical depending on the chosen model. To overcome this issue, this paper presents a Ridge-penalised spectral least-squares estimation method for second-order stationary point processes. This is achieved through two novel approaches: a p-thinning-based cross-validation method to tune the penalisation parameter, relying on the spectral representation of the process; and the introduction of a spectral least-squares contrast based around the asymptotic properties of the periodogram of the sample. The proposed method is then illustrated by a simulation study on linear Hawkes processes in the context of parametric estimation, highlighting its performances against more traditional approaches, specifically when working with short observation windows.",
    "arxiv_id": "2601.07490",
    "authors": [
      "Miguel Martinez Herrera",
      "Felix Cheysson"
    ],
    "categories": [
      "stat.ME"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Ridge-penalised spectral least-squares estimation for point processes",
    "url": "https://arxiv.org/abs/2601.07490",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f28a1e83-897d-4e93-a54d-7d0a1a1ad538",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}