Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "9950"
  #_source: array:26 [
    "id" => "9950"
    "slug" => "bootstrap-based-q%cc%82kh2-for-the-selection-of-components-and-variables-in-pls-regression"
    "yearMonth" => "2003-10"
    "year" => "2003"
    "title" => "Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression"
    "description" => "AMATO, S. et ESPOSITO VINZI, V. (2003). Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression. <i>Chemometrics and Intelligent Laboratory Systems</i>, 68(43862), pp. 5-16."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "ESPOSITO VINZI Vincenzo"
        "bid" => "B00067049"
        "slug" => "esposito-vinzi-vincenzo"
      ]
      1 => array:1 [
        "name" => "AMATO Silvano"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:5 [
      0 => "Variable and Component Selection"
      1 => "RegressionPartial Least Squares Regression"
      2 => "Irrelevant factors"
      3 => "Bootstrap"
      4 => "Cross-validation"
    ]
    "updatedAt" => "2022-12-05 14:03:54"
    "publicationUrl" => "https://doi.org/10.1016/S0169-7439(03)00083-2"
    "publicationInfo" => array:3 [
      "pages" => "5-16"
      "volume" => "68"
      "number" => "43862"
    ]
    "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 aim of this paper is to suggest a bootstrap-based method for choosing the number of components in Partial Least Squares Regression (PLSR). Cross-validated Qh2 statistic is used, for which is intended to derive a bootstrap distribution and to perform a hypothesis testing. Monte Carlo approximation is adopted. Applications on both artificial and real data are presented."
      "en" => "The aim of this paper is to suggest a bootstrap-based method for choosing the number of components in Partial Least Squares Regression (PLSR). Cross-validated Qh2 statistic is used, for which is intended to derive a bootstrap distribution and to perform a hypothesis testing. Monte Carlo approximation is adopted. Applications on both artificial and real data are presented."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-22T03:21:45.000Z"
    "docTitle" => "Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, AMATO Silvano"
    "docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo, AMATO Silvano</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2003</span>"
    "keywordList" => "<a href="#">Variable and Component Selection</a>, <a href="#">RegressionPartial Least Squares Regression</a>, <a href="#">Irrelevant factors</a>, <a href="#">Bootstrap</a>, <a href="#">Cross-validation</a>"
    "docPreview" => "<b>Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression</b><br><span>2003-10 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/S0169-7439(03)00083-2" target="_blank">Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.957939
  +"parent": null
}