<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-18T07:24:54Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:10230/70521" metadataPrefix="qdc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:10230/70521</identifier><datestamp>2025-05-27T23:28:03Z</datestamp><setSpec>com_2072_6</setSpec><setSpec>col_2072_452952</setSpec></header><metadata><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>Mean estimation in high dimension</dc:title>
   <dc:creator>Lugosi, Gábor</dc:creator>
   <dc:subject>Mean estimation</dc:subject>
   <dc:subject>Robustness</dc:subject>
   <dc:subject>High-dimensional statistics</dc:subject>
   <dcterms:abstract>In this note we discuss the statistical problem of estimating the mean of a random vector based on independent, identically distributed data. This classical problem has recently attracted a lot of attention both in mathematical statistics and in theoretical computer science and numerous intricacies have been revealed. We discuss some of the recent advances, focusing on high-dimensional aspects.</dcterms:abstract>
   <dcterms:abstract>This work was supported by the Spanish Ministry of Economy and Competitiveness, Grant PGC2018-101643-B-I00 and by “Google Focused Award Algorithms and Learning for AI”.</dcterms:abstract>
   <dcterms:dateAccepted>2025-05-27T23:28:03Z</dcterms:dateAccepted>
   <dcterms:available>2025-05-27T23:28:03Z</dcterms:available>
   <dcterms:created>2025-05-27T23:28:03Z</dcterms:created>
   <dcterms:issued>2025-05-27T06:54:56Z</dcterms:issued>
   <dcterms:issued>2025-05-27T06:54:56Z</dcterms:issued>
   <dcterms:issued>2022</dcterms:issued>
   <dc:type>info:eu-repo/semantics/bookPart</dc:type>
   <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
   <dc:identifier>http://hdl.handle.net/10230/70521</dc:identifier>
   <dc:relation>Beliaev D, Smirnov S, editors. International Congress of Mathematicians: 2022 Jul 6-14. Berlin: International Mathematical Union; 2023. p. 5500-14</dc:relation>
   <dc:relation>info:eu-repo/grantAgreement/ES/2PE/PGC2018-101643-B-I00</dc:relation>
   <dc:rights>Published by EMS Press and licensed under a CC BY 4.0 license.</dc:rights>
   <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
   <dc:publisher>EMS Press</dc:publisher>
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