Essec\Faculty\Model\Contribution {#2233
#_index: "academ_contributions"
#_id: "16052"
#_source: array:26 [
"id" => "16052"
"slug" => "16052-regmmd-a-package-for-parametric-estimation-and-regression-with-maximum-mean-discrepancy"
"yearMonth" => "2025-11"
"year" => "2025"
"title" => "regMMD: a package for parametric estimation and regression with maximum mean discrepancy"
"description" => "ALQUIER, P. et GERBER, M. (2025). regMMD: a package for parametric estimation and regression with maximum mean discrepancy. <i>Computo</i>, In press."
"authors" => array:2 [
0 => array:3 [
"name" => "ALQUIER Pierre"
"bid" => "B00809923"
"slug" => "alquier-pierre"
]
1 => array:1 [
"name" => "GERBER Mathieu"
]
]
"ouvrage" => ""
"keywords" => array:6 [
0 => "parameter estimation"
1 => "regression"
2 => "robust statistics"
3 => "minimum distance estimation"
4 => "kernel methods"
5 => "maximum mean discrepancy"
]
"updatedAt" => "2025-11-20 11:32:48"
"publicationUrl" => "https://doi.org/10.57750/d6d1-gb09"
"publicationInfo" => array:3 [
"pages" => ""
"volume" => "In press"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => "France"
"en" => "France"
]
"abstract" => array:2 [
"fr" => "The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for parametric estimation, as it has been shown to yield robust estimators. We have implemented MMD minimization for parameter inference in a wide range of statistical models, including various regression models, within an R package called regMMD. This paper provides an introduction to the regMMD package. We describe the available kernels and optimization procedures, as well as the default settings. Detailed applications to simulated and real data are provided."
"en" => "The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for parametric estimation, as it has been shown to yield robust estimators. We have implemented MMD minimization for parameter inference in a wide range of statistical models, including various regression models, within an R package called regMMD. This paper provides an introduction to the regMMD package. We describe the available kernels and optimization procedures, as well as the default settings. Detailed applications to simulated and real data are provided."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-12-06T08:21:43.000Z"
"docTitle" => "regMMD: a package for parametric estimation and regression with maximum mean discrepancy"
"docSurtitle" => "Articles"
"authorNames" => "<a href="/cv/alquier-pierre">ALQUIER Pierre</a>, GERBER Mathieu"
"docDescription" => "<span class="document-property-authors">ALQUIER Pierre, GERBER Mathieu</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2025</span>"
"keywordList" => "<a href="#">parameter estimation</a>, <a href="#">regression</a>, <a href="#">robust statistics</a>, <a href="#">minimum distance estimation</a>, <a href="#">kernel methods</a>, <a href="#">maximum mean discrepancy</a>"
"docPreview" => "<b>regMMD: a package for parametric estimation and regression with maximum mean discrepancy</b><br><span>2025-11 | Articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.57750/d6d1-gb09" target="_blank">regMMD: a package for parametric estimation and regression with maximum mean discrepancy</a>"
]
+lang: "fr"
+"_score": 8.687645
+"_ignored": array:2 [
0 => "abstract.en.keyword"
1 => "abstract.fr.keyword"
]
+"parent": null
}
