Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "13913"
#_source: array:26 [
"id" => "13913"
"slug" => "sparsity-considerations-for-dependent-variables"
"yearMonth" => "2011-08"
"year" => "2011"
"title" => "Sparsity considerations for dependent variables"
"description" => "ALQUIER, P. et DOUKHAN, P. (2011). Sparsity considerations for dependent variables. <i>The Electronic Journal of Statistics</i>, 5, pp. 750-774."
"authors" => array:2 [
0 => array:3 [
"name" => "ALQUIER Pierre"
"bid" => "B00809923"
"slug" => "alquier-pierre"
]
1 => array:1 [
"name" => "DOUKHAN Paul"
]
]
"ouvrage" => ""
"keywords" => array:8 [
0 => "Density estimation"
1 => "deviation of empirical mean"
2 => "Estimation in high dimension"
3 => "Lasso"
4 => "Penalization"
5 => "Regression estimation"
6 => "Sparsity"
7 => "Weak dependence"
]
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-5/issue-none/Sparsity-considerations-for-dependent-variables/10.1214/11-EJS626.full"
"publicationInfo" => array:3 [
"pages" => "750-774"
"volume" => "5"
"number" => null
]
"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 provide a comprehensive introduction for the study of ℓ1-penalized estimators in the context of dependent observations. We define a general ℓ1-penalized estimator for solving problems of stochastic optimization. This estimator turns out to be the LASSO [Tib96] in the regression estimation setting. Powerful theoretical guarantees on the statistical performances of the LASSO were provided in recent papers, however, they usually only deal with the iid case. Here, we study this estimator under various dependence assumptions."
"en" => "The aim of this paper is to provide a comprehensive introduction for the study of ℓ1-penalized estimators in the context of dependent observations. We define a general ℓ1-penalized estimator for solving problems of stochastic optimization. This estimator turns out to be the LASSO [Tib96] in the regression estimation setting. Powerful theoretical guarantees on the statistical performances of the LASSO were provided in recent papers, however, they usually only deal with the iid case. Here, we study this estimator under various dependence assumptions."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-21T09:21:53.000Z"
"docTitle" => "Sparsity considerations for dependent variables"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, DOUKHAN Paul"
"docDescription" => "<span class="document-property-authors">ALQUIER Pierre, DOUKHAN Paul</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2011</span>"
"keywordList" => "<a href="#">Density estimation</a>, <a href="#">deviation of empirical mean</a>, <a href="#">Estimation in high dimension</a>, <a href="#">Lasso</a>, <a href="#">Penalization</a>, <a href="#">Regression estimation</a>, <a href="#">Sparsity</a>, <a href="#">Weak dependence</a>"
"docPreview" => "<b>Sparsity considerations for dependent variables</b><br><span>2011-08 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-5/issue-none/Sparsity-considerations-for-dependent-variables/10.1214/11-EJS626.full" target="_blank">Sparsity considerations for dependent variables</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.554104
+"parent": null
}
