Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "12536"
#_source: array:26 [
"id" => "12536"
"slug" => "12536-on-parameter-estimation-with-the-wasserstein-distance"
"yearMonth" => "2019-10"
"year" => "2019"
"title" => "On parameter estimation with the Wasserstein distance"
"description" => "BERNTON, E., JACOB, P., GERBER, M. et ROBERT, C.P. (2019). On parameter estimation with the Wasserstein distance. <i>Information and Inference: A Journal of the IMA</i>, 8(4), pp. 657-676. 
BERNTON, E., JACOB, P., GERBER, M. et ROBERT, C.P. (2019). On parameter estimation with the Wasserst "
"authors" => array:4 [
0 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
1 => array:1 [
"name" => "BERNTON Espen"
]
2 => array:1 [
"name" => "GERBER Mathieu"
]
3 => array:1 [
"name" => "ROBERT Christian P"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://doi.org/10.1093/imaiai/iaz003"
"publicationInfo" => array:3 [
"pages" => "657-676"
"volume" => "8"
"number" => "4"
]
"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" => "Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We study asymptotic properties of such minimum Wasserstein distance estimators, complementing results derived by Bassetti, Bodini and Regazzini in 2006. In particular, our results cover the misspecified setting, in which the data-generating process is not assumed to be part of the family of distributions described by the model. Our results are motivated by recent applications of minimum Wasserstein estimators to complex generative models. We discuss some difficulties arising in the numerical approximation of these estimators. Two of our numerical examples (g-and-κ and sum of log-normals) are taken from the literature on approximate Bayesian computation and have likelihood functions that are not analytically tractable. Two other examples involve misspecified models. Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein dist "
"en" => "Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We study asymptotic properties of such minimum Wasserstein distance estimators, complementing results derived by Bassetti, Bodini and Regazzini in 2006. In particular, our results cover the misspecified setting, in which the data-generating process is not assumed to be part of the family of distributions described by the model. Our results are motivated by recent applications of minimum Wasserstein estimators to complex generative models. We discuss some difficulties arising in the numerical approximation of these estimators. Two of our numerical examples (g-and-κ and sum of log-normals) are taken from the literature on approximate Bayesian computation and have likelihood functions that are not analytically tractable. Two other examples involve misspecified models. Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein dist "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-03-16T09:21:41.000Z"
"docTitle" => "On parameter estimation with the Wasserstein distance"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, BERNTON Espen, GERBER Mathieu, ROBERT Christian P"
"docDescription" => "<span class="document-property-authors">JACOB Pierre, BERNTON Espen, GERBER Mathieu, ROBERT Christian P</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2019</span> <span class="document-property-authors">JACOB Pierre, BERNTON Espen, GERBER Mathieu, ROBERT Christia "
"keywordList" => ""
"docPreview" => "<b>On parameter estimation with the Wasserstein distance</b><br><span>2019-10 | Journal articles </span> <b>On parameter estimation with the Wasserstein distance</b><br><span>2019-10 | Journal articles </s "
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1093/imaiai/iaz003" target="_blank">On parameter estimation with the Wasserstein distance</a> <a href="https://doi.org/10.1093/imaiai/iaz003" target="_blank">On parameter estimation with the Was "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.635794
+"parent": null
}
