Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "13430"
#_source: array:26 [
"id" => "13430"
"slug" => "13430-multi-normex-distributions-for-the-sum-of-random-vectors-rates-of-convergence"
"yearMonth" => "2023-01"
"year" => "2023"
"title" => "Multi-normex distributions for the sum of random vectors. Rates of convergence"
"description" => "KRATZ, M. et PROKOPENKO, E. (2023). Multi-normex distributions for the sum of random vectors. Rates of convergence. <i>Extremes</i>, 26, pp. 509-544. 
KRATZ, M. et PROKOPENKO, E. (2023). Multi-normex distributions for the sum of random vectors. Rates "
"authors" => array:2 [
0 => array:3 [
"name" => "KRATZ Marie"
"bid" => "B00072305"
"slug" => "kratz-marie"
]
1 => array:1 [
"name" => "PROKOPENKO Evgeny"
]
]
"ouvrage" => ""
"keywords" => array:10 [
0 => "aggregation"
1 => "central limit theorem"
2 => "dependence"
3 => "extreme value theorem"
4 => "geometrical quantiles"
5 => "multivariate regular variation"
6 => "(multivariate) Pareto distribution"
7 => "ordered statistics"
8 => "QQ-plots"
9 => "rate of convergence"
]
"updatedAt" => "2024-03-18 09:59:05"
"publicationUrl" => "https://link.springer.com/article/10.1007/s10687-022-00461-7"
"publicationInfo" => array:3 [
"pages" => "509-544"
"volume" => "26"
"number" => ""
]
"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" => "We build a sharp approximation of the whole distribution of the sum of iid heavy-tailed random vectors, combining mean and extreme behaviors. It extends the so-called ’normex’ approach from a univariate to a multivariate framework. We propose two possible multi-normex distributions, named d-Normex and MRV-Normex. Both rely on the Gaussian distribution for describing the mean behavior, via the CLT, while the difference between the two versions comes from using the exact distribution or the EV theorem for the maximum. The main theorems provide the rate of convergence for each version of the multi-normex distributions towards the distribution of the sum, assuming second order regular variation property for the norm of the parent random vector when considering the MRV-normex case. Numerical illustrations and comparisons are proposed with various dependence structures on the parent random vector, using QQ-plots based on geometrical quantiles. We build a sharp approximation of the whole distribution of the sum of iid heavy-tailed random vecto "
"en" => "We build a sharp approximation of the whole distribution of the sum of iid heavy-tailed random vectors, combining mean and extreme behaviors. It extends the so-called ’normex’ approach from a univariate to a multivariate framework. We propose two possible multi-normex distributions, named d-Normex and MRV-Normex. Both rely on the Gaussian distribution for describing the mean behavior, via the CLT, while the difference between the two versions comes from using the exact distribution or the EV theorem for the maximum. The main theorems provide the rate of convergence for each version of the multi-normex distributions towards the distribution of the sum, assuming second order regular variation property for the norm of the parent random vector when considering the MRV-normex case. Numerical illustrations and comparisons are proposed with various dependence structures on the parent random vector, using QQ-plots based on geometrical quantiles. We build a sharp approximation of the whole distribution of the sum of iid heavy-tailed random vecto "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-21T00:21:45.000Z"
"docTitle" => "Multi-normex distributions for the sum of random vectors. Rates of convergence"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/kratz-marie">KRATZ Marie</a>, PROKOPENKO Evgeny"
"docDescription" => "<span class="document-property-authors">KRATZ Marie, PROKOPENKO Evgeny</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2023</span> <span class="document-property-authors">KRATZ Marie, PROKOPENKO Evgeny</span><br><span class="docume "
"keywordList" => "<a href="#">aggregation</a>, <a href="#">central limit theorem</a>, <a href="#">dependence</a>, <a href="#">extreme value theorem</a>, <a href="#">geometrical quantiles</a>, <a href="#">multivariate regular variation</a>, <a href="#">(multivariate) Pareto distribution</a>, <a href="#">ordered statistics</a>, <a href="#">QQ-plots</a>, <a href="#">rate of convergence</a> <a href="#">aggregation</a>, <a href="#">central limit theorem</a>, <a href="#">dependence</a>, <a h "
"docPreview" => "<b>Multi-normex distributions for the sum of random vectors. Rates of convergence</b><br><span>2023-01 | Journal articles </span> <b>Multi-normex distributions for the sum of random vectors. Rates of convergence</b><br><span>2023- "
"docType" => "research"
"publicationLink" => "<a href="https://link.springer.com/article/10.1007/s10687-022-00461-7" target="_blank">Multi-normex distributions for the sum of random vectors. Rates of convergence</a> <a href="https://link.springer.com/article/10.1007/s10687-022-00461-7" target="_blank">Multi-normex "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.619822
+"parent": null
}
