{"id":"https://openalex.org/W2045297649","doi":"https://doi.org/10.1109/tit.2014.2346194","title":"Optimal Grouping for Group Minimax Hypothesis Testing","display_name":"Optimal Grouping for Group Minimax Hypothesis Testing","publication_year":2014,"publication_date":"2014-08-07","ids":{"openalex":"https://openalex.org/W2045297649","doi":"https://doi.org/10.1109/tit.2014.2346194","mag":"2045297649"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2014.2346194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2014.2346194","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1307.6512","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015286159","display_name":"Kush R. Varshney","orcid":"https://orcid.org/0000-0002-7376-5536"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]},{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kush R. Varshney","raw_affiliation_strings":["Mathematical Science and Analytics Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","IBM, ,"],"affiliations":[{"raw_affiliation_string":"Mathematical Science and Analytics Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM, ,","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065423139","display_name":"Lav R. Varshney","orcid":"https://orcid.org/0000-0003-2798-5308"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lav R. Varshney","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA","Department of Electrical and Computer Engineering, University of Illinois at Urbana\u2014Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Illinois at Urbana\u2014Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5015286159"],"corresponding_institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12705425,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"60","issue":"10","first_page":"6511","last_page":"6521"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9916999936103821,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9911999702453613,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.8747039437294006},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7758740782737732},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6929153800010681},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.5420047640800476},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5209583044052124},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.46396127343177795},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.43159613013267517},{"id":"https://openalex.org/keywords/bregman-divergence","display_name":"Bregman divergence","score":0.4282233715057373},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4187428951263428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34455394744873047},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3193643093109131},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2948707342147827},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2844628393650055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27283966541290283},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25136351585388184},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.21223697066307068}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.8747039437294006},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7758740782737732},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6929153800010681},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.5420047640800476},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5209583044052124},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46396127343177795},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.43159613013267517},{"id":"https://openalex.org/C149073432","wikidata":"https://www.wikidata.org/wiki/Q4960382","display_name":"Bregman divergence","level":2,"score":0.4282233715057373},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4187428951263428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34455394744873047},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3193643093109131},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2948707342147827},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2844628393650055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27283966541290283},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25136351585388184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.21223697066307068},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tit.2014.2346194","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2014.2346194","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1307.6512","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1307.6512","pdf_url":"https://arxiv.org/pdf/1307.6512","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2045297649","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1307.6512","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.745.1258","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.745.1258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1307.6512.pdf","raw_type":"text"},{"id":"doi:10.48550/arxiv.1307.6512","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1307.6512","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1307.6512","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1307.6512","pdf_url":"https://arxiv.org/pdf/1307.6512","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W40692021","https://openalex.org/W112198115","https://openalex.org/W1543800554","https://openalex.org/W1576475658","https://openalex.org/W1590693676","https://openalex.org/W1599798800","https://openalex.org/W1634005169","https://openalex.org/W1859602357","https://openalex.org/W1965761421","https://openalex.org/W1972295533","https://openalex.org/W1974127894","https://openalex.org/W1979268836","https://openalex.org/W1995219511","https://openalex.org/W1998386146","https://openalex.org/W2016190290","https://openalex.org/W2025899035","https://openalex.org/W2036240054","https://openalex.org/W2040689732","https://openalex.org/W2075057405","https://openalex.org/W2079313765","https://openalex.org/W2079819958","https://openalex.org/W2096191798","https://openalex.org/W2096613063","https://openalex.org/W2096765209","https://openalex.org/W2097117108","https://openalex.org/W2099924374","https://openalex.org/W2107467870","https://openalex.org/W2116327295","https://openalex.org/W2126425523","https://openalex.org/W2130700086","https://openalex.org/W2131116400","https://openalex.org/W2133953323","https://openalex.org/W2140942991","https://openalex.org/W2141493900","https://openalex.org/W2143470941","https://openalex.org/W2149639256","https://openalex.org/W2150526113","https://openalex.org/W2165240636","https://openalex.org/W4230690448","https://openalex.org/W4234239782","https://openalex.org/W4239022279","https://openalex.org/W4239966440","https://openalex.org/W4246498846","https://openalex.org/W4251566724","https://openalex.org/W4254197176","https://openalex.org/W6635823994","https://openalex.org/W6674201379","https://openalex.org/W6891829388"],"related_works":["https://openalex.org/W2646437084","https://openalex.org/W2952520328","https://openalex.org/W2792686747","https://openalex.org/W2072549527","https://openalex.org/W2088063661","https://openalex.org/W141020401","https://openalex.org/W2126399888","https://openalex.org/W2913790168","https://openalex.org/W136316033","https://openalex.org/W2337194294","https://openalex.org/W2181968062","https://openalex.org/W2059202432","https://openalex.org/W2268735772","https://openalex.org/W3098952422","https://openalex.org/W2549952436","https://openalex.org/W3126774347","https://openalex.org/W2978292356","https://openalex.org/W2609865181","https://openalex.org/W2104510459","https://openalex.org/W1512485015"],"abstract_inverted_index":{"Bayesian":[0,41],"hypothesis":[1,5,44,56],"testing":[2,6,45],"and":[3,23,42,121,178,191,216],"minimax":[4,43,138,145],"represent":[7],"extreme":[8],"instances":[9],"of":[10,17,54,79,87,111,114,163,166,198,204,207,217],"detection":[11,105,199],"in":[12,63],"which":[13,64],"the":[14,18,55,65,77,83,93,109,112,126,152,160,164,175,202],"prior":[15,66,115,167],"probabilities":[16,116,168],"hypotheses":[19],"are":[20,27,95,181,221],"either":[21],"completely":[22,28],"precisely":[24],"known,":[25],"or":[26,50,85],"unknown.":[29],"Group":[30],"minimax,":[31,72],"also":[32,122],"known":[33],"as":[34],"Gamma-minimax,":[35],"is":[36,90,169],"a":[37,102,134,144,170,193],"robust":[38],"intermediary":[39],"between":[40],"that":[46,92,117,130],"allows":[47],"for":[48,156],"coarse":[49],"partial":[51],"advance":[52],"knowledge":[53],"priors":[57,124],"by":[58,211],"using":[59],"information":[60],"on":[61,70],"sets":[62,84],"lies.":[67],"Existing":[68],"work":[69],"group":[71,137],"however,":[73],"does":[74],"not":[75],"consider":[76],"question":[78],"how":[80],"to":[81,140,186],"define":[82],"groups":[86,94,120],"priors;":[88],"it":[89],"assumed":[91],"given.":[96],"In":[97],"this":[98,157],"work,":[99],"we":[100],"propose":[101],"novel":[103],"intermediate":[104],"scheme":[106],"formulated":[107],"through":[108],"quantization":[110,135],"space":[113,165],"optimally":[118],"determines":[119],"representative":[123],"within":[125],"groups.":[127,205],"We":[128],"show":[129],"when":[131],"viewed":[132],"from":[133],"perspective,":[136],"amounts":[139],"determining":[141],"centroids":[142],"with":[143,184,201],"Bayes":[146,187],"risk":[147,188],"error":[148,189],"divergence":[149,155],"distortion":[150],"criterion:":[151],"appropriate":[153],"Bregman":[154,171],"task.":[158],"Moreover,":[159],"optimal":[161,176],"partitioning":[162],"Voronoi":[172],"diagram.":[173],"Together,":[174],"grouping":[177],"representation":[179],"points":[180],"an":[182],"epsilon-net":[183],"respect":[185],"divergence,":[190],"permit":[192],"rate-distortion":[194],"type":[195],"asymptotic":[196],"analysis":[197],"performance":[200],"number":[203],"Examples":[206],"detecting":[208],"signals":[209,220],"corrupted":[210],"additive":[212],"white":[213],"Gaussian":[214],"noise":[215],"distinguishing":[218],"exponentially-distributed":[219],"presented.":[222]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
