Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "13976"
  #_source: array:26 [
    "id" => "13976"
    "slug" => "estimation-of-copulas-via-maximum-mean-discrepancy"
    "yearMonth" => "2023-07"
    "year" => "2023"
    "title" => "Estimation of Copulas via Maximum Mean Discrepancy"
    "description" => "ALQUIER, P., CHERIEF-ABDELLATIF, B.E., DERUMIGNY, A. et FERMANIAN, J.D. (2023). Estimation of Copulas via Maximum Mean Discrepancy. <i>Journal of the American Statistical Association</i>, 118(543), pp. 1997-2012."
    "authors" => array:4 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:2 [
        "name" => "CHERIEF-ABDELLATIF Badr-Eddine"
        "bid" => "B00810114"
      ]
      2 => array:1 [
        "name" => "DERUMIGNY Alexis"
      ]
      3 => array:1 [
        "name" => "FERMANIAN Jean-David"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Algorithms semiparametric inference"
      1 => "Copula"
      2 => "Kernel methods and RKHS"
      3 => "Robust procedures"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1080/01621459.2021.2024836"
    "publicationInfo" => array:3 [
      "pages" => "1997-2012"
      "volume" => "118"
      "number" => "543"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification."
      "en" => "This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-21T16:21:43.000Z"
    "docTitle" => "Estimation of Copulas via Maximum Mean Discrepancy"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, CHERIEF-ABDELLATIF Badr-Eddine, DERUMIGNY Alexis, FERMANIAN Jean-David"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, CHERIEF-ABDELLATIF Badr-Eddine, DERUMIGNY Alexis, FERMANIAN Jean-David</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2023</span>"
    "keywordList" => "<a href="#">Algorithms semiparametric inference</a>, <a href="#">Copula</a>, <a href="#">Kernel methods and RKHS</a>, <a href="#">Robust procedures</a>"
    "docPreview" => "<b>Estimation of Copulas via Maximum Mean Discrepancy</b><br><span>2023-07 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1080/01621459.2021.2024836" target="_blank">Estimation of Copulas via Maximum Mean Discrepancy</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.758286
  +"parent": null
}