Essec\Faculty\Model\Contribution {#2190
  #_index: "academ_contributions"
  #_id: "10431"
  #_source: array:26 [
    "id" => "10431"
    "slug" => "rank-penalized-estimators-for-high-dimensional-matrices"
    "yearMonth" => "2011-10"
    "year" => "2011"
    "title" => "Rank penalized estimators for high-dimensional matrices"
    "description" => "KLOPP, O. (2011). Rank penalized estimators for high-dimensional matrices. <i>The Electronic Journal of Statistics</i>, 5, pp. 1161-1183."
    "authors" => array:1 [
      0 => array:3 [
        "name" => "KLOPP Olga"
        "bid" => "B00732676"
        "slug" => "klopp-olga"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2021-07-13 14:31:35"
    "publicationUrl" => "https://projecteuclid.org/download/pdfview_1/euclid.ejs/1317906992"
    "publicationInfo" => array:3 [
      "pages" => "1161-1183"
      "volume" => "5"
      "number" => null
    ]
    "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" => """
        In this paper we consider the trace regression model. Assume\n
        that we observe a small set of entries or linear combinations of entries of an\n
        unknown matrix A0 corrupted by noise. We propose a new rank penalized\n
        estimator of A0. For this estimator we establish general oracle inequality for\n
        the prediction error both in probability and in expectation. We also prove\n
        upper bounds for the rank of our estimator. Then, we apply our general\n
        results to the problems of matrix completion and matrix regression. In\n
        these cases our estimator has a particularly simple form: it is obtained by\n
        hard thresholding of the singular values of a matrix constructed from the\n
        observations.
        """
      "en" => """
        In this paper we consider the trace regression model. Assume\n
        that we observe a small set of entries or linear combinations of entries of an\n
        unknown matrix A0 corrupted by noise. We propose a new rank penalized\n
        estimator of A0. For this estimator we establish general oracle inequality for\n
        the prediction error both in probability and in expectation. We also prove\n
        upper bounds for the rank of our estimator. Then, we apply our general\n
        results to the problems of matrix completion and matrix regression. In\n
        these cases our estimator has a particularly simple form: it is obtained by\n
        hard thresholding of the singular values of a matrix constructed from the\n
        observations.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-19T22:21:59.000Z"
    "docTitle" => "Rank penalized estimators for high-dimensional matrices"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>"
    "docDescription" => "<span class="document-property-authors">KLOPP Olga</span><br><span class="document-property-authors_fields">Information Systems, Decision Sciences and Statistics</span> | <span class="document-property-year">2011</span>"
    "keywordList" => ""
    "docPreview" => "<b>Rank penalized estimators for high-dimensional matrices</b><br><span>2011-10 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://projecteuclid.org/download/pdfview_1/euclid.ejs/1317906992" target="_blank">Rank penalized estimators for high-dimensional matrices</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 9.101864
  +"parent": null
}