Essec\Faculty\Model\Contribution {#2233 ▼
#_index: "academ_contributions"
#_id: "10082"
#_source: array:26 [
"id" => "10082"
"slug" => "10082-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. 
ESPOSITO VINZI, V., LAURO, C. et AMATO, S. (2005). PLS Typological Regression: Algorithmic, Classifi "
"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 http://www.springer.com/west/home/statistics/business?SGWID=4-10135-22-40802327-0&bcsi_scan_FFDAA255 "
"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 Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA me
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 the double objective of describing the set of explanatory variables while predicting the set of resp
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 algorithm is developed that allows to build typologies of statistical units whose different local PL
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 initial global PLS model. The typology induced by the algorithm may undergo a non parametric validat
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 Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA me
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 the double objective of describing the set of explanatory variables while predicting the set of resp
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 algorithm is developed that allows to build typologies of statistical units whose different local PL
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 initial global PLS model. The typology induced by the algorithm may undergo a non parametric validat
a compromise model is investigated.
"""
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-01T08:21:43.000Z"
"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, Data Analytics et Opérations</span> | <span class="document-property-year">2005</span> <span class="document-property-authors">ESPOSITO VINZI Vincenzo, LAURO Carlo, AMATO Silvano</span><b "
"keywordList" => "<a href="#">Partial Least Squares</a>, <a href="#">Regression</a>, <a href="#">ClassificationDistance from the model</a>, <a href="#">Prediction-oriented classes</a> <a href="#">Partial Least Squares</a>, <a href="#">Regression</a>, <a href="#">ClassificationDistanc "
"docPreview" => "<b>PLS Typological Regression: Algorithmic, Classification and Validation Issues</b><br><span>2005-04 | Chapitres </span> <b>PLS Typological Regression: Algorithmic, Classification and Validation Issues</b><br><span>2005-0 "
"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> <a href="http://www.springer.com/west/home/statistics/business?SGWID=4-10135-22-40802327-0&bcsi_scan "
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.654378
+"parent": null
}
