Essec\Faculty\Model\Contribution {#2186
  #_index: "academ_contributions"
  #_id: "10672"
  #_source: array:26 [
    "id" => "10672"
    "slug" => "high-dimensional-matrix-estimation-with-unknown-variance-of-the-noise"
    "yearMonth" => "2017-01"
    "year" => "2017"
    "title" => "High dimensional matrix estimation with unknown variance of the noise"
    "description" => "KLOPP, O. et GAIFFAS, S. (2017). High dimensional matrix estimation with unknown variance of the noise. <i>Statistica Sinica</i>, 27(1), pp. 115-145."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "KLOPP Olga"
        "bid" => "B00732676"
        "slug" => "klopp-olga"
      ]
      1 => array:1 [
        "name" => "GAIFFAS Stefano"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2021-09-24 10:33:27"
    "publicationUrl" => "https://hal.archives-ouvertes.fr/hal-00649437v4/document"
    "publicationInfo" => array:3 [
      "pages" => "115-145"
      "volume" => "27"
      "number" => "1"
    ]
    "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" => """
        Assume that we observe a small set of entries or linear combinations of entries\n
        of an unknown matrix A corrupted by noise. We propose a new method for\n
        estimating A which does not rely on the knowledge or on an estimation of the\n
        standard deviation of the noise s. Our estimator achieves, up to a logarithmic\n
        factor, optimal rates of convergence under the Frobenius risk and, thus, has the\n
        same prediction performance as previously proposed estimators which rely on the\n
        knowledge of s. Some numerical experiments show the benefits of this approach.
        """
      "en" => """
        Assume that we observe a small set of entries or linear combinations of entries\n
        of an unknown matrix A corrupted by noise. We propose a new method for\n
        estimating A which does not rely on the knowledge or on an estimation of the\n
        standard deviation of the noise s. Our estimator achieves, up to a logarithmic\n
        factor, optimal rates of convergence under the Frobenius risk and, thus, has the\n
        same prediction performance as previously proposed estimators which rely on the\n
        knowledge of s. Some numerical experiments show the benefits of this approach.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-23T07:22:03.000Z"
    "docTitle" => "High dimensional matrix estimation with unknown variance of the noise"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>, GAIFFAS Stefano"
    "docDescription" => "<span class="document-property-authors">KLOPP Olga, GAIFFAS Stefano</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2017</span>"
    "keywordList" => ""
    "docPreview" => "<b>High dimensional matrix estimation with unknown variance of the noise</b><br><span>2017-01 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://hal.archives-ouvertes.fr/hal-00649437v4/document" target="_blank">High dimensional matrix estimation with unknown variance of the noise</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.994421
  +"parent": null
}