Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "13909"
  #_source: array:26 [
    "id" => "13909"
    "slug" => "prediction-of-quantiles-by-statistical-learning-and-application-to-gdp-forecasting"
    "yearMonth" => "2012-10"
    "year" => "2012"
    "title" => "Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting"
    "description" => "ALQUIER, P. et LI, X. (2012). Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting. Dans: <i>15th International Conference on Discovery Science (DS'12)</i>. Lyon:  Springer Berlin Heidelberg, pp. 22-36."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ALQUIER Pierre"
        "bid" => "B00809923"
        "slug" => "alquier-pierre"
      ]
      1 => array:1 [
        "name" => "LI Xiang"
      ]
    ]
    "ouvrage" => "15th International Conference on Discovery Science (DS'12)"
    "keywords" => []
    "updatedAt" => "2023-03-22 13:57:13"
    "publicationUrl" => "https://link.springer.com/chapter/10.1007/978-3-642-33492-4_5"
    "publicationInfo" => array:3 [
      "pages" => "22-36"
      "volume" => ""
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "n this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of [1], this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results."
      "en" => "n this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of [1], this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results."
    ]
    "authors_fields" => array:2 [
      "fr" => "Management"
      "en" => "Management"
    ]
    "indexedAt" => "2024-11-21T09:21:53.000Z"
    "docTitle" => "Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting"
    "docSurtitle" => "Conference Proceedings"
    "authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, LI Xiang"
    "docDescription" => "<span class="document-property-authors">ALQUIER Pierre, LI Xiang</span><br><span class="document-property-authors_fields">Management</span> | <span class="document-property-year">2012</span>"
    "keywordList" => ""
    "docPreview" => "<b>Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting</b><br><span>2012-10 | Conference Proceedings </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://link.springer.com/chapter/10.1007/978-3-642-33492-4_5" target="_blank">Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.21763
  +"parent": null
}