Essec\Faculty\Model\Contribution {#6196
  #_index: "academ_contributions"
  #_id: "10082"
  #_source: array:26 [
    "id" => "10082"
    "slug" => "pls-typological-regression-algorithmic-classification-and-validation-issues"
    "yearMonth" => "2005-04"
    "year" => "2005"
    "title" => "PLS Typological Regression: Algorithmic, Classification and Validation Issues"
    "description" => "ESPOSITO VINZI, V., LAURO, C. et AMATO, S. (2005). PLS Typological Regression: Algorithmic, Classification and Validation Issues. Dans: <i>New Developments in Classification and Data Analysis</i>. 1st ed. New York: Springer, pp. 133-140."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ESPOSITO VINZI Vincenzo"
        "bid" => "B00067049"
        "slug" => "esposito-vinzi-vincenzo"
      ]
      1 => array:1 [
        "name" => "LAURO Carlo"
      ]
      2 => array:1 [
        "name" => "AMATO Silvano"
      ]
    ]
    "ouvrage" => "New Developments in Classification and Data Analysis"
    "keywords" => array:4 [
      0 => "Partial Least Squares"
      1 => "Regression"
      2 => "ClassificationDistance from the model"
      3 => "Prediction-oriented classes"
    ]
    "updatedAt" => "2020-12-17 18:37:46"
    "publicationUrl" => "http://www.springer.com/west/home/statistics/business?SGWID=4-10135-22-40802327-0&bcsi_scan_FFDAA2550BA9B46D=XiU7zpker8w4jETC9+lRmQMAAADvAU4A"
    "publicationInfo" => array:3 [
      "pages" => "133-140"
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Chapitres"
      "en" => "Book chapters"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA methodology, i.e. as the assignment of statistical units to a-priori defined classes. As a matter of fact, PLS components are built with\n
        the double objective of describing the set of explanatory variables while predicting the set of response variables. Taking into account this objective, a classification\n
        algorithm is developed that allows to build typologies of statistical units whose different local PLS models have an intrinsic explanatory power higher than the\n
        initial global PLS model. The typology induced by the algorithm may undergo a non parametric validation procedure based on bootstrap. Finally, the definition of\n
        a compromise model is investigated.
        """
      "en" => """
        Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA methodology, i.e. as the assignment of statistical units to a-priori defined classes. As a matter of fact, PLS components are built with\n
        the double objective of describing the set of explanatory variables while predicting the set of response variables. Taking into account this objective, a classification\n
        algorithm is developed that allows to build typologies of statistical units whose different local PLS models have an intrinsic explanatory power higher than the\n
        initial global PLS model. The typology induced by the algorithm may undergo a non parametric validation procedure based on bootstrap. Finally, the definition of\n
        a compromise model is investigated.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-18T21:22:06.000Z"
    "docTitle" => "PLS Typological Regression: Algorithmic, Classification and Validation Issues"
    "docSurtitle" => "Chapitres"
    "authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, LAURO Carlo, AMATO Silvano"
    "docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo, LAURO Carlo, AMATO Silvano</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2005</span>"
    "keywordList" => "<a href="#">Partial Least Squares</a>, <a href="#">Regression</a>, <a href="#">ClassificationDistance from the model</a>, <a href="#">Prediction-oriented classes</a>"
    "docPreview" => "<b>PLS Typological Regression: Algorithmic, Classification and Validation Issues</b><br><span>2005-04 | Chapitres </span>"
    "docType" => "research"
    "publicationLink" => "<a href="http://www.springer.com/west/home/statistics/business?SGWID=4-10135-22-40802327-0&bcsi_scan_FFDAA2550BA9B46D=XiU7zpker8w4jETC9+lRmQMAAADvAU4A" target="_blank">PLS Typological Regression: Algorithmic, Classification and Validation Issues</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 9.133676
  +"parent": null
}