Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13908"
  #_source: array:26 [
    "id" => "13908"
    "slug" => "pac-bayesian-estimation-and-prediction-in-sparse-additive-models"
    "yearMonth" => "2013-01"
    "year" => "2013"
    "title" => "PAC-Bayesian estimation and prediction in sparse additive models"
    "description" => "GUEDJ, B. et ALQUIER, P. (2013). PAC-Bayesian estimation and prediction in sparse additive models. <i>The Electronic Journal of Statistics</i>, 7, pp. 264-291."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "GUEDJ Benjamin"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:7 [
      0 => "Additive models"
      1 => "MCMC"
      2 => "Oracle inequality"
      3 => "PAC-Bayesian bounds"
      4 => "Regression estimation"
      5 => "Sparsity"
      6 => "stochastic search"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-7/issue-none/PAC-Bayesian-estimation-and-prediction-in-sparse-additive-models/10.1214/13-EJS771.full"
    "publicationInfo" => array:3 [
      "pages" => "264-291"
      "volume" => "7"
      "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" => "The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p ≫ n paradigm). A PAC-Bayesian strategy is investigated, delivering oracle inequalities in probability. The implementation is performed through recent outcomes in high-dimensional MCMC algorithms, and the performance of our method is assessed on simulated data."
      "en" => "The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p ≫ n paradigm). A PAC-Bayesian strategy is investigated, delivering oracle inequalities in probability. The implementation is performed through recent outcomes in high-dimensional MCMC algorithms, and the performance of our method is assessed on simulated data."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T09:21:53.000Z"
    "docTitle" => "PAC-Bayesian estimation and prediction in sparse additive models"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, GUEDJ Benjamin"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, GUEDJ Benjamin</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2013</span>"
    "keywordList" => "<a href="#">Additive models</a>, <a href="#">MCMC</a>, <a href="#">Oracle inequality</a>, <a href="#">PAC-Bayesian bounds</a>, <a href="#">Regression estimation</a>, <a href="#">Sparsity</a>, <a href="#">stochastic search</a>"
    "docPreview" => "<b>PAC-Bayesian estimation and prediction in sparse additive models</b><br><span>2013-01 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-7/issue-none/PAC-Bayesian-estimation-and-prediction-in-sparse-additive-models/10.1214/13-EJS771.full" target="_blank">PAC-Bayesian estimation and prediction in sparse additive models</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.554104
  +"parent": null
}