Essec\Faculty\Model\Contribution {#2233
#_index: "academ_contributions"
#_id: "13920"
#_source: array:26 [
"id" => "13920"
"slug" => "constributions-to-statistical-learning-in-sparse-models"
"yearMonth" => "2013-12"
"year" => "2013"
"title" => "Constributions to Statistical Learning in Sparse Models"
"description" => "ALQUIER, P. (2013). Constributions to Statistical Learning in Sparse Models. Paris: France."
"authors" => array:1 [
0 => array:3 [
"name" => "ALQUIER Pierre"
"bid" => "B00809923"
"slug" => "alquier-pierre"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2024-10-31 13:51:19"
"publicationUrl" => "http://tel.archives-ouvertes.fr/tel-00915505"
"publicationInfo" => array:3 [
"pages" => null
"volume" => null
"number" => null
]
"type" => array:2 [
"fr" => "HDR"
"en" => "HDR"
]
"support_type" => array:2 [
"fr" => null
"en" => null
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "The aim of this habilitation thesis is to give an overview of my works on high-dimensional statistics and statistical learning, under various sparsity assumptions. In a first part, I will describe the major challenges of high-dimensional statistics in the context of the generic linear regression model. After a brief review of existing results, I will present the theoretical study of aggregated estimators that was done in (Alquier & Lounici 2011). The second part essentially aims at providing extensions of the various theories presented in the first part to the estimation of time series models (Alquier & Doukhan 2011, Alquier & Wintenberger 2013, Alquier & Li 2012, Alquier, Wintenberger & Li 2012). Finally, the third part presents various extensions to nonparametric models, or to specific applications such as quantum statistics (Alquier & Biau 2013, Guedj & Alquier 2013, Alquier, Meziani & Peyré 2013, Alquier, Butucea, Hebiri, Meziani & Morimae 2013, Alquier 2013, Alquier 2008). In each section, we provide explicitely the estimators used and, as much as possible, optimal oracle inequalities satisfied by these estimators."
"en" => "The aim of this habilitation thesis is to give an overview of my works on high-dimensional statistics and statistical learning, under various sparsity assumptions. In a first part, I will describe the major challenges of high-dimensional statistics in the context of the generic linear regression model. After a brief review of existing results, I will present the theoretical study of aggregated estimators that was done in (Alquier & Lounici 2011). The second part essentially aims at providing extensions of the various theories presented in the first part to the estimation of time series models (Alquier & Doukhan 2011, Alquier & Wintenberger 2013, Alquier & Li 2012, Alquier, Wintenberger & Li 2012). Finally, the third part presents various extensions to nonparametric models, or to specific applications such as quantum statistics (Alquier & Biau 2013, Guedj & Alquier 2013, Alquier, Meziani & Peyré 2013, Alquier, Butucea, Hebiri, Meziani & Morimae 2013, Alquier 2013, Alquier 2008). In each section, we provide explicitely the estimators used and, as much as possible, optimal oracle inequalities satisfied by these estimators."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-11-08T20:21:42.000Z"
"docTitle" => "Constributions to Statistical Learning in Sparse Models"
"docSurtitle" => "HDR"
"authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>"
"docDescription" => "<span class="document-property-authors">ALQUIER Pierre</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2013</span>"
"keywordList" => ""
"docPreview" => "<b>Constributions to Statistical Learning in Sparse Models</b><br><span>2013-12 | HDR </span>"
"docType" => "research"
"publicationLink" => "<a href="http://tel.archives-ouvertes.fr/tel-00915505" target="_blank">Constributions to Statistical Learning in Sparse Models</a>"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 9.001675
+"parent": null
}