Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "12663"
  #_source: array:26 [
    "id" => "12663"
    "slug" => "maximum-likelihood-estimation-of-sparse-networks-with-missing-observations"
    "yearMonth" => "2021-12"
    "year" => "2021"
    "title" => "Maximum likelihood estimation of sparse networks with missing observations"
    "description" => "GAUCHER, S. et KLOPP, O. (2021). Maximum likelihood estimation of sparse networks with missing observations. <i>Journal of Statistical Planning and Inference</i>, 215, pp. 299-329."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "KLOPP Olga"
        "bid" => "B00732676"
        "slug" => "klopp-olga"
      ]
      1 => array:1 [
        "name" => "GAUCHER Solenne"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Missing observations"
      1 => "Network models"
      2 => "Sparse estimation"
      3 => "Graphon model"
      4 => "Variational approximation"
    ]
    "updatedAt" => "2023-01-27 01:00:40"
    "publicationUrl" => "https://www.sciencedirect.com/science/article/pii/S0378375821000422#!"
    "publicationInfo" => array:3 [
      "pages" => "299-329"
      "volume" => "215"
      "number" => ""
    ]
    "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" => "Estimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon model and the inhomogeneous random graph model with missing observations. Using the Stochastic Block Model as a parametric proxy, we bound the risk of the maximum likelihood estimator of network connections probabilities, and show that it is minimax optimal. Moreover, we show that our estimator can be efficiently approximated using tractable variational methods, and thus used in practice."
      "en" => "Estimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon model and the inhomogeneous random graph model with missing observations. Using the Stochastic Block Model as a parametric proxy, we bound the risk of the maximum likelihood estimator of network connections probabilities, and show that it is minimax optimal. Moreover, we show that our estimator can be efficiently approximated using tractable variational methods, and thus used in practice."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T10:21:50.000Z"
    "docTitle" => "Maximum likelihood estimation of sparse networks with missing observations"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>, GAUCHER Solenne"
    "docDescription" => "<span class="document-property-authors">KLOPP Olga, GAUCHER Solenne</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2021</span>"
    "keywordList" => "<a href="#">Missing observations</a>, <a href="#">Network models</a>, <a href="#">Sparse estimation</a>, <a href="#">Graphon model</a>, <a href="#">Variational approximation</a>"
    "docPreview" => "<b>Maximum likelihood estimation of sparse networks with missing observations</b><br><span>2021-12 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.sciencedirect.com/science/article/pii/S0378375821000422#!" target="_blank">Maximum likelihood estimation of sparse networks with missing observations</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.554104
  +"parent": null
}