{
  "annotation_id": "f611297c-dde9-4333-bcdc-31dc3933fd72",
  "date_created": "2026-02-17T05:53:08.604000Z",
  "date_modified": "2026-02-17T05:53:08.604000Z",
  "file_hash": "e5b64d1d22381d071dc328238cf3b6c76668b5d997e71e65117449054e00958f",
  "private": false,
  "record": {
    "abstract": "This work deals with the estimation of parameters of Mittag-Leffler (ML($\\alpha, \\sigma$)) distribution. We estimate the parameters of ML($\\alpha, \\sigma$) using empirical Laplace transform method. The simulation study indicates that the proposed method provides satisfactory results. The real life application of ML($\\alpha, \\sigma$) distribution on high frequency trading data is also demonstrated. We also provide the estimation of three-parameter Mittag-Leffler distribution using empirical Laplace transform. Additionally, we establish an autoregressive model of order 1, incorporating the Mittag-Leffler distribution as marginals in one scenario and as innovation terms in another. We apply empirical Laplace transform method to estimate the model parameters and provide the simulation study for the same.",
    "arxiv_id": "2601.06610",
    "authors": [
      "Monika S. Dhull"
    ],
    "categories": [
      "stat.ME"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Mittag Leffler Distributions Estimation and Autoregressive Framework",
    "url": "https://arxiv.org/abs/2601.06610",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b45ba68b-c14c-4941-af54-0f6df2d78802",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}