Essec\Faculty\Model\Contribution {#2205
  #_index: "academ_contributions"
  #_id: "5414"
  #_source: array:26 [
    "id" => "5414"
    "slug" => "an-integrated-pls-regression-based-approach-for-multidimensional-blocks-in-pls-path-modeling"
    "yearMonth" => "2010-05"
    "year" => "2010"
    "title" => "An integrated PLS Regression-based approach for multidimensional blocks in PLS Path Modeling"
    "description" => "ESPOSITO VINZI, V., RUSSOLILLO, G. et TRINCHERA, L. (2010). An integrated PLS Regression-based approach for multidimensional blocks in PLS Path Modeling."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ESPOSITO VINZI Vincenzo"
        "bid" => "B00067049"
        "slug" => "esposito-vinzi-vincenzo"
      ]
      1 => array:1 [
        "name" => "RUSSOLILLO G."
      ]
      2 => array:1 [
        "name" => "TRINCHERA L."
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2021-04-19 17:57:25"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Communications dans une conférence"
      "en" => "Presentations at an Academic or Professional conference"
    ]
    "support_type" => array:2 [
      "fr" => null
      "en" => null
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "We present how the integration of PLS regression modules within the algorithm of PLS Path Modeling can be very helpful to improve performance of predictive path models between blocks of variables related to different concepts. The objective is to develop analyses that take into account the multidimensionality of the different blocks as well as the prediction performance of the specified model."
      "en" => "We present how the integration of PLS regression modules within the algorithm of PLS Path Modeling can be very helpful to improve performance of predictive path models between blocks of variables related to different concepts. The objective is to develop analyses that take into account the multidimensionality of the different blocks as well as the prediction performance of the specified model."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d’Information, Sciences de la Décision et Statistiques"
      "en" => "Information Systems, Decision Sciences and Statistics"
    ]
    "indexedAt" => "2024-04-24T15:22:17.000Z"
    "docTitle" => "An integrated PLS Regression-based approach for multidimensional blocks in PLS Path Modeling"
    "docSurtitle" => "Communications dans une conférence"
    "authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, RUSSOLILLO G., TRINCHERA L."
    "docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo, RUSSOLILLO G., TRINCHERA L.</span><br><span class="document-property-authors_fields">Systèmes d’Information, Sciences de la Décision et Statistiques</span> | <span class="document-property-year">2010</span>"
    "keywordList" => ""
    "docPreview" => "<b>An integrated PLS Regression-based approach for multidimensional blocks in PLS Path Modeling</b><br><span>2010-05 | Communications dans une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">An integrated PLS Regression-based approach for multidimensional blocks in PLS Path Modeling</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.718075
  +"parent": null
}