Essec\Faculty\Model\Contribution {#2233 ▼
#_index: "academ_contributions"
#_id: "6373"
#_source: array:26 [
"id" => "6373"
"slug" => "6373-latent-segments-detection-in-pls-path-modeling-a-tool-to-capture-unobserved-heterogeneity-in-customers-preferences 
6373-latent-segments-detection-in-pls-path-modeling-a-tool-to-capture-unobserved-heterogeneity-in-cu "
"yearMonth" => "2007-09"
"year" => "2007"
"title" => "Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Customers' Preferences Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Custom "
"description" => "TRINCHERA, L., ROMANO, R. et ESPOSITO VINZI, V. (2007). Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Customers' Preferences. TRINCHERA, L., ROMANO, R. et ESPOSITO VINZI, V. (2007). Latent Segments Detection in PLS Path Modeli "
"authors" => array:3 [
0 => array:3 [
"name" => "ESPOSITO VINZI Vincenzo"
"bid" => "B00067049"
"slug" => "esposito-vinzi-vincenzo"
]
1 => array:1 [
"name" => "TRINCHERA L."
]
2 => array:1 [
"name" => "ROMANO R."
]
]
"ouvrage" => ""
"keywords" => array:3 [
0 => "REBUS"
1 => "Response Based Classification"
2 => "Unobserved Heterogeneity"
]
"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" => "PLS Path Modeling represents a useful tool to model complex causal relations between blocks of variables in customers' preferences studies. When customers do have different behaviors, models accounting for unobserved heterogeneity permit to outline targeted and more efficient strategies, while a single model for all the customers might hide relevant differences in their behaviors. Unobserved heterogeneity implies to identify segments of units that exhibit similar behaviors, i.e. a response-based classification. This approach requires to look for latent segments as well as to estimate 'local' models. Different methods have been proposed for capturing unobserved heterogeneity in PLS Path Modeling. In this paper, the REsponse Based Units Segmentation (REBUS-PLS) approach is presented. REBUS-PLS is a distribution-free approach that aims to yield a classification of units which leads to 'local' models with an improved predictive performance as compared to a global model. PLS Path Modeling represents a useful tool to model complex causal relations between blocks of varia "
"en" => "PLS Path Modeling represents a useful tool to model complex causal relations between blocks of variables in customers' preferences studies. When customers do have different behaviors, models accounting for unobserved heterogeneity permit to outline targeted and more efficient strategies, while a single model for all the customers might hide relevant differences in their behaviors. Unobserved heterogeneity implies to identify segments of units that exhibit similar behaviors, i.e. a response-based classification. This approach requires to look for latent segments as well as to estimate 'local' models. Different methods have been proposed for capturing unobserved heterogeneity in PLS Path Modeling. In this paper, the REsponse Based Units Segmentation (REBUS-PLS) approach is presented. REBUS-PLS is a distribution-free approach that aims to yield a classification of units which leads to 'local' models with an improved predictive performance as compared to a global model. PLS Path Modeling represents a useful tool to model complex causal relations between blocks of varia "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-01T11:21:46.000Z"
"docTitle" => "Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Customers' Preferences Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Custom "
"docSurtitle" => "Communications dans une conférence"
"authorNames" => "<a href="/cv/esposito-vinzi-vincenzo">ESPOSITO VINZI Vincenzo</a>, TRINCHERA L., ROMANO R."
"docDescription" => "<span class="document-property-authors">ESPOSITO VINZI Vincenzo, TRINCHERA L., ROMANO R.</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2007</span> <span class="document-property-authors">ESPOSITO VINZI Vincenzo, TRINCHERA L., ROMANO R.</span><br>< "
"keywordList" => "<a href="#">REBUS</a>, <a href="#">Response Based Classification</a>, <a href="#">Unobserved Heterogeneity</a> <a href="#">REBUS</a>, <a href="#">Response Based Classification</a>, <a href="#">Unobserved Heterog "
"docPreview" => "<b>Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Customers' Preferences</b><br><span>2007-09 | Communications dans une conférence </span> <b>Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Cus "
"docType" => "research"
"publicationLink" => "<a href="#" target="_blank">Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobserved Heterogeneity in Customers' Preferences</a> <a href="#" target="_blank">Latent Segments Detection in PLS Path Modeling: A Tool to Capture Unobse "
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.975039
+"parent": null
}
