{"id":"https://openalex.org/W2976319582","doi":"https://doi.org/10.1109/isit.2019.8849279","title":"Mean estimation for entangled single-sample distributions","display_name":"Mean estimation for entangled single-sample distributions","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2976319582","doi":"https://doi.org/10.1109/isit.2019.8849279","mag":"2976319582"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2019.8849279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849279","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046067745","display_name":"Ankit Pensia","orcid":"https://orcid.org/0000-0003-3509-1224"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ankit Pensia","raw_affiliation_strings":["Department of Computer Science, University of Wisconsin, Madison"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021502006","display_name":"Varun Jog","orcid":"https://orcid.org/0000-0003-4159-0900"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Varun Jog","raw_affiliation_strings":["Department of Electrical & Computer Engineering, University of Wisconsin, Madison","Department of Electrical & Computer Engineering, University of Wisconsin, Madison"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"Department of Electrical & Computer Engineering, University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023368186","display_name":"Po\u2010Ling Loh","orcid":"https://orcid.org/0000-0002-6514-7834"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Po-Ling Loh","raw_affiliation_strings":["Department of Computer Science, University of Wisconsin, Madison","Department of Statistics, University of Wisconsin, Madison"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"Department of Statistics, University of Wisconsin, Madison","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046067745"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":0.4337,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72258887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3052","last_page":"3056"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.982699990272522,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9811000227928162,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.491030216217041},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.48687830567359924},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4173422157764435},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15811771154403687},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08034121990203857}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.491030216217041},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.48687830567359924},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4173422157764435},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15811771154403687},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08034121990203857},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2019.8849279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2019.8849279","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W305865050","https://openalex.org/W306662213","https://openalex.org/W568673721","https://openalex.org/W1574816920","https://openalex.org/W1780272748","https://openalex.org/W1956647075","https://openalex.org/W1969015668","https://openalex.org/W1995642533","https://openalex.org/W2009209064","https://openalex.org/W2018944012","https://openalex.org/W2019704260","https://openalex.org/W2046033161","https://openalex.org/W2051726817","https://openalex.org/W2110455165","https://openalex.org/W2141195893","https://openalex.org/W2214878575","https://openalex.org/W2273081434","https://openalex.org/W2787248994","https://openalex.org/W2802066458","https://openalex.org/W3016189756","https://openalex.org/W4212909578","https://openalex.org/W4250954493","https://openalex.org/W6638034133","https://openalex.org/W6676525959","https://openalex.org/W6693858544","https://openalex.org/W7002181973"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"We":[0,35],"consider":[1],"the":[2,6,24,42,47,53,61,80,94,97],"problem":[3],"of":[4,9,44,63,79,96],"estimating":[5],"common":[7],"mean":[8],"univariate":[10],"data,":[11,48],"when":[12],"independent":[13],"samples":[14,28],"are":[15,29],"drawn":[16],"from":[17,87],"non-identical":[18],"symmetric,":[19],"unimodal":[20],"distributions.":[21],"This":[22],"captures":[23],"setting":[25,55],"where":[26,60],"all":[27],"Gaussian":[30],"with":[31,107],"different":[32],"unknown":[33],"variances.":[34],"propose":[36],"an":[37],"estimator":[38,75],"that":[39],"adapts":[40],"to":[41],"level":[43],"heterogeneity":[45],"in":[46,51],"achieving":[49],"near-optimality":[50],"both":[52],"i.i.d.":[54],"and":[56,84],"some":[57],"heterogeneous":[58],"settings,":[59],"fraction":[62],"\u201clow-noise\"":[64],"points":[65],"is":[66,76],"as":[67,69],"small":[68],"log":[70],"n":[71,72],".":[73],"Our":[74],"a":[77],"hybrid":[78],"modal":[81],"interval,":[82],"shorth,":[83],"median":[85],"estimators":[86,102],"classical":[88],"statistics.":[89],"The":[90],"rates":[91],"depend":[92],"on":[93],"percentile":[95],"mixture":[98],"distribution,":[99],"making":[100],"our":[101],"useful":[103],"even":[104],"for":[105],"distributions":[106],"infinite":[108],"variance.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}