{
  "abstract": "We propose a multivariate two-population mixture distribution for heavy-tailed and positively skewed non-negative data. The first mixture population is a multivariate lognormal, while the second is a distribution obtained by joining univariate zero-mean generalized Pareto densities via a Gumbel copula. The model is unsupervised, since no threshold has to be set or estimated. We derive some properties of the distribution and develop an Expectation-Conditional Maximization algorithm for maximum likelihood estimation, which is found to perform well in multivariate contexts beyond the bivariate case. The finite-sample properties of the estimators are assessed by means of simulation experiments, and an empirical application to aggregated commercial property-casualty insurance claims illustrates the impact of dependence on risk estimation. All the methods are implemented in the R package MultLognGPD, available on Github.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.02148",
  "author_total": 2,
  "authors": [
    {
      "name": "Marco Bee"
    },
    {
      "name": "Flavio Santi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0pdmx9vkqy02p.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.02148",
  "fulltext_url": null,
  "id": "0pdmx9vkqy02p",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.02148",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.02148",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0pdmx9vkqy02p.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.02148",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.02148"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "A multivariate unsupervised lognormal-GPD mixture for dependent losses",
  "title_text": "A multivariate unsupervised lognormal-GPD mixture for dependent losses",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/0pdmx9vkqy02p",
  "venue": "arXiv",
  "versions": []
}