Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "10686"
#_source: array:26 [
"id" => "10686"
"slug" => "10686-adaptive-confidence-sets-for-matrix-completion"
"yearMonth" => "2018-03"
"year" => "2018"
"title" => "Adaptive confidence sets for matrix completion"
"description" => "KLOPP, O., CARPENTIER, A., LÖFFLER, M. et NICKL, R. (2018). Adaptive confidence sets for matrix completion. <i>Bernoulli: A Journal of Mathematical Statistics and Probability</i>, 24(4A), pp. 2429-2460. 
KLOPP, O., CARPENTIER, A., LÖFFLER, M. et NICKL, R. (2018). Adaptive confidence sets for matrix comp "
"authors" => array:4 [
0 => array:3 [
"name" => "KLOPP Olga"
"bid" => "B00732676"
"slug" => "klopp-olga"
]
1 => array:1 [
"name" => "CARPENTIER Alexandra"
]
2 => array:1 [
"name" => "LÖFFLER Mattias"
]
3 => array:1 [
"name" => "NICKL R."
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://projecteuclid.org/euclid.bj/1522051214"
"publicationInfo" => array:3 [
"pages" => "2429-2460"
"volume" => "24"
"number" => "4A"
]
"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" => "In the present paper, we study the problem of existence of honest and adaptive confidence sets for matrix completion. We consider two statistical models: the trace regression model and the Bernoulli model. In the trace regression model, we show that honest confidence sets that adapt to the unknown rank of the matrix exist even when the error variance is unknown. Contrary to this, we prove that in the Bernoulli model, honest and adaptive confidence sets exist only when the error variance is known a priori. In the course of our proofs, we obtain bounds for the minimax rates of certain composite hypothesis testing problems arising in low rank inference. In the present paper, we study the problem of existence of honest and adaptive confidence sets for m "
"en" => "In the present paper, we study the problem of existence of honest and adaptive confidence sets for matrix completion. We consider two statistical models: the trace regression model and the Bernoulli model. In the trace regression model, we show that honest confidence sets that adapt to the unknown rank of the matrix exist even when the error variance is unknown. Contrary to this, we prove that in the Bernoulli model, honest and adaptive confidence sets exist only when the error variance is known a priori. In the course of our proofs, we obtain bounds for the minimax rates of certain composite hypothesis testing problems arising in low rank inference. In the present paper, we study the problem of existence of honest and adaptive confidence sets for m "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-12T05:21:40.000Z"
"docTitle" => "Adaptive confidence sets for matrix completion"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>, CARPENTIER Alexandra, LÖFFLER Mattias, NICKL R."
"docDescription" => "<span class="document-property-authors">KLOPP Olga, CARPENTIER Alexandra, LÖFFLER Mattias, NICKL R.</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2018</span> <span class="document-property-authors">KLOPP Olga, CARPENTIER Alexandra, LÖFFLER Mattias, NICKL R.< "
"keywordList" => ""
"docPreview" => "<b>Adaptive confidence sets for matrix completion</b><br><span>2018-03 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://projecteuclid.org/euclid.bj/1522051214" target="_blank">Adaptive confidence sets for matrix completion</a> <a href="https://projecteuclid.org/euclid.bj/1522051214" target="_blank">Adaptive confidence sets fo "
]
+lang: "en"
+"_type": "_doc"
+"_score": 9.030495
+"parent": null
}
