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Matrix Estimation Meets Statistical Network Analysis: Extracting low-dimensional structures in high dimension

dc.date.accessioned2019-10-24T15:45:08Z
dc.date.available2019-10-24T15:45:08Z
dc.date.issued2018
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3651
dc.description.abstractThe study of complex relationships among the elements of a large collection of random variables lead to the development of a number of areas in probability and statistics such as probabilistic network analysis or random matrix theory. The aim of the workshop was to address the challenge to develop a coherent mathematical framework within which these areas can be integrated, for a successful analysis of massive and complicated data sets.
dc.titleMatrix Estimation Meets Statistical Network Analysis: Extracting low-dimensional structures in high dimension
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWR-2018-29
local.series.idOWR-2018-29
local.subject.msc62
local.subject.msc60
local.sortindex1091
local.date-range17 Jun - 23 Jun 2018
local.workshopcode1825b
local.workshoptitleMatrix Estimation Meets Statistical Network Analysis: Extracting low-dimensional structures in high dimension
local.organizersFlorentina Bunea, Ithaca; Angelika Rohde, Freiburg; Patrick Wolfe, London; Harrison Zhou, New Haven
local.report-nameWorkshop Report 2018,29
local.opc-photo-id1825b
local.publishers-doi10.4171/OWR/2018/29
local.ems-referenceBunea Florentina, Rohde Angelika, Wolfe Patrick, Zhou Harrison: Matrix Estimation Meets Statistical Network Analysis: Extracting low-dimensional structures in high dimension. Oberwolfach Rep. 15 (2018), 1745-1783. doi: 10.4171/OWR/2018/29


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