Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "16222"
  #_source: array:26 [
    "id" => "16222"
    "slug" => "16222-multivariate-discrete-generalized-pareto-distributions-theory-simulation-and-applications-to-dry-spells"
    "yearMonth" => "2025-10"
    "year" => "2025"
    "title" => "Multivariate discrete Generalized Pareto Distributions: Theory, simulation, and applications to dry spells"
    "description" => "AKA, S., KRATZ, M. et NAVEAU, P. (2025). <i>Multivariate discrete Generalized Pareto Distributions: Theory, simulation, and applications to dry spells</i>. WP 2508, ESSEC Business School."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "KRATZ Marie"
        "bid" => "B00072305"
        "slug" => "kratz-marie"
      ]
      1 => array:1 [
        "name" => "AKA Samira"
      ]
      2 => array:1 [
        "name" => "NAVEAU Philippe"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "discrete multivariate extremes"
      1 => "exceedances"
      2 => "drought analysis"
      3 => """
        neural Bayes\n
        estimator
        """
    ]
    "updatedAt" => "2026-03-02 11:47:01"
    "publicationUrl" => "https://essec.hal.science/hal-05131737v2"
    "publicationInfo" => array:3 [
      "pages" => ""
      "volume" => ""
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Documents de travail"
      "en" => "Working Papers"
    ]
    "support_type" => array:2 [
      "fr" => "Cahier de Recherche"
      "en" => "Working Papers"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        This article extends the multivariate extreme value theory (MEVT) to discrete settings, focusing on the generalized Pareto distribution (GPD) as a foundational tool.\n
        The purpose of the study is to enhance the understanding of extreme discrete count data representation, particularly for discrete exceedances over thresholds, defining and using multivariate discrete Pareto distributions (MDGPD). Through theoretical results and illustrative examples, we outline the construction and properties of MDGPDs, providing practical insights into simulation techniques and data fitting\n
        approaches using recent likelihood-free inference methods. This framework broadens the toolkit for modeling extreme events, offering robust methodologies for analyzing multivariate discrete data with extreme values. To illustrate its practical relevance, we present an application of this method to drought analysis, addressing a growing concern in Europe.
        """
      "en" => """
        This article extends the multivariate extreme value theory (MEVT) to discrete settings, focusing on the generalized Pareto distribution (GPD) as a foundational tool.\n
        The purpose of the study is to enhance the understanding of extreme discrete count data representation, particularly for discrete exceedances over thresholds, defining and using multivariate discrete Pareto distributions (MDGPD). Through theoretical results and illustrative examples, we outline the construction and properties of MDGPDs, providing practical insights into simulation techniques and data fitting\n
        approaches using recent likelihood-free inference methods. This framework broadens the toolkit for modeling extreme events, offering robust methodologies for analyzing multivariate discrete data with extreme values. To illustrate its practical relevance, we present an application of this method to drought analysis, addressing a growing concern in Europe.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2026-03-08T02:21:59.000Z"
    "docTitle" => "Multivariate discrete Generalized Pareto Distributions: Theory, simulation, and applications to dry spells"
    "docSurtitle" => "Working Papers"
    "authorNames" => "<a href="/cv/kratz-marie">KRATZ Marie</a>, AKA Samira, NAVEAU Philippe"
    "docDescription" => "<span class="document-property-authors">KRATZ Marie, AKA Samira, NAVEAU Philippe</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2025</span>"
    "keywordList" => """
      <a href="#">discrete multivariate extremes</a>, <a href="#">exceedances</a>, <a href="#">drought analysis</a>, <a href="#">neural Bayes\n
      estimator</a>
      """
    "docPreview" => "<b>Multivariate discrete Generalized Pareto Distributions: Theory, simulation, and applications to dry spells</b><br><span>2025-10 | Working Papers </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://essec.hal.science/hal-05131737v2" target="_blank">Multivariate discrete Generalized Pareto Distributions: Theory, simulation, and applications to dry spells</a>"
  ]
  +lang: "en"
  +"_score": 8.712892
  +"_ignored": array:2 [
    0 => "abstract.en.keyword"
    1 => "abstract.fr.keyword"
  ]
  +"parent": null
}