{"id":"https://openalex.org/W2007545461","doi":"https://doi.org/10.1214/09-aos718","title":"Asymptotic inference for high-dimensional data","display_name":"Asymptotic inference for high-dimensional data","publication_year":2010,"publication_date":"2010-02-19","ids":{"openalex":"https://openalex.org/W2007545461","doi":"https://doi.org/10.1214/09-aos718","mag":"2007545461"},"language":"en","primary_location":{"id":"doi:10.1214/09-aos718","doi":"https://doi.org/10.1214/09-aos718","is_oa":false,"landing_page_url":"https://doi.org/10.1214/09-aos718","pdf_url":null,"source":{"id":"https://openalex.org/S119757635","display_name":"The Annals of Statistics","issn_l":"0090-5364","issn":["0090-5364","2168-8966"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a-star","anvur-class-a","anvur-scientific","cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","norway-2","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Annals of Statistics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1002.4554","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jim Kuelbs","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jim Kuelbs","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Anand N. Vidyashankar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anand N. Vidyashankar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3466,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83808326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"38","issue":"2","first_page":"836","last_page":"869"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9937000274658203,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9937000274658203,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.0019000000320374966,"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/T11716","display_name":"Random Matrices and Applications","score":0.0010999999940395355,"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/high-dimensional-data","display_name":"high-dimensional data","score":0.9509999752044678},{"id":"https://openalex.org/keywords/asymptotic-inference","display_name":"asymptotic inference","score":0.8759999871253967},{"id":"https://openalex.org/keywords/missing-data","display_name":"missing data","score":0.9160000085830688},{"id":"https://openalex.org/keywords/asymptotic-normality","display_name":"asymptotic normality","score":0.9819999933242798},{"id":"https://openalex.org/keywords/mean-vector-estimation","display_name":"mean vector estimation","score":0.6349999904632568},{"id":"https://openalex.org/keywords/one-sample-tests","display_name":"one-sample tests","score":0.6700000166893005},{"id":"https://openalex.org/keywords/two-sample-test","display_name":"two-sample test","score":0.996999979019165},{"id":"https://openalex.org/keywords/robustness","display_name":"robustness","score":0.878000020980835}],"concepts":[{"id":"https://openalex.org/C65778772","wikidata":"https://www.wikidata.org/wiki/Q12345341","display_name":"Asymptotic distribution","level":3,"score":0.7261000275611877},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6466000080108643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6122000217437744},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5756000280380249},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5529999732971191},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5080000162124634},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.5062999725341797},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4268999993801117},{"id":"https://openalex.org/C136336373","wikidata":"https://www.wikidata.org/wiki/Q5611303","display_name":"Grouped data","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C119047807","wikidata":"https://www.wikidata.org/wiki/Q752718","display_name":"Asymptotic analysis","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3594000041484833},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3237000107765198},{"id":"https://openalex.org/C2780012671","wikidata":"https://www.wikidata.org/wiki/Q1343870","display_name":"Corollary","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.2782000005245209},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C181789720","wikidata":"https://www.wikidata.org/wiki/Q4812191","display_name":"Asymptotically optimal algorithm","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C31388003","wikidata":"https://www.wikidata.org/wiki/Q7624548","display_name":"Strong consistency","level":3,"score":0.2538999915122986},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.25049999356269836}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1214/09-aos718","doi":"https://doi.org/10.1214/09-aos718","is_oa":false,"landing_page_url":"https://doi.org/10.1214/09-aos718","pdf_url":null,"source":{"id":"https://openalex.org/S119757635","display_name":"The Annals of Statistics","issn_l":"0090-5364","issn":["0090-5364","2168-8966"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a-star","anvur-class-a","anvur-scientific","cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","norway-2","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The Annals of Statistics","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1002.4554","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1002.4554","pdf_url":"https://arxiv.org/pdf/1002.4554","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"pmh:oai:CULeuclid:euclid.aos/1266586616","doi":null,"is_oa":false,"landing_page_url":"http://projecteuclid.org/euclid.aos/1266586616","pdf_url":null,"source":{"id":"https://openalex.org/S4306400787","display_name":"Project Euclid (Cornell University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"doi:10.48550/arxiv.1002.4554","doi":"https://doi.org/10.48550/arxiv.1002.4554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1002.4554","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1002.4554","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1002.4554","pdf_url":"https://arxiv.org/pdf/1002.4554","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W361471363","https://openalex.org/W1573241294","https://openalex.org/W1966701961","https://openalex.org/W2007545461","https://openalex.org/W2017413905","https://openalex.org/W2018000058","https://openalex.org/W2022345949","https://openalex.org/W2060705109","https://openalex.org/W2062125287","https://openalex.org/W2074063815","https://openalex.org/W2109363337","https://openalex.org/W2113304782","https://openalex.org/W3118259273","https://openalex.org/W4238472918"],"related_works":[],"abstract_inverted_index":{"In":[0,21,88],"this":[1],"paper,":[2],"we":[3,23,74,91,126,153],"study":[4,29],"inference":[5],"for":[6,78,120,131,164],"high-dimensional":[7],"data":[8,165,213],"characterized":[9],"by":[10],"small":[11],"sample":[12,45],"sizes":[13],"relative":[14],"to":[15,28,50,158,201],"the":[16,19,38,44,69,72,79,83,86,89,97,104,110,114,137,149,174,197,206],"dimension":[17],"of":[18,40,59,65,71,82,85,96,109,116,148,188,191,211],"data.":[20,61],"particular,":[22],"provide":[24,75],"an":[25,160],"infinite-dimensional":[26,150],"framework":[27],"statistical":[30],"models":[31],"that":[32,101],"involve":[33],"situations":[34,172],"in":[35,103,205],"which":[36,192],"(i)":[37],"number":[39],"parameters":[41],"increase":[42],"with":[43],"size":[46],"(that":[47],"is,":[48],"allowed":[49],"be":[51,177],"random)":[52],"and":[53,93,133,140],"(ii)":[54],"there":[55],"is":[56,113,214],"a":[57,63,124,146,186],"possibility":[58],"missing":[60],"Under":[62],"variety":[64,187],"tail":[66],"conditions":[67,77],"on":[68],"components":[70],"data,":[73],"precise":[76],"joint":[80],"consistency":[81,99],"estimators":[84],"mean.":[87],"process,":[90],"clarify":[92],"improve":[94],"some":[95,190],"recent":[98],"results":[100,119,157,168],"appeared":[102],"literature.":[105],"An":[106],"important":[107],"aspect":[108],"work":[111],"presented":[112,169],"development":[115],"asymptotic":[117,143],"normality":[118],"these":[121,155],"models.":[122],"As":[123],"consequence,":[125],"construct":[127],"different":[128,195],"test":[129],"statistics":[130],"one-sample":[132],"two-sample":[134],"problems":[135],"concerning":[136],"mean":[138],"vector":[139],"obtain":[141],"their":[142],"distributions":[144],"as":[145],"corollary":[147],"results.":[151],"Finally,":[152],"use":[154],"theoretical":[156],"develop":[159],"asymptotically":[161],"justifiable":[162],"methodology":[163,175],"analyses.":[166],"Simulation":[167],"here":[170],"describe":[171],"where":[173],"can":[176],"successfully":[178],"applied.":[179],"They":[180],"also":[181,215],"evaluate":[182],"its":[183],"robustness":[184],"under":[185],"conditions,":[189],"are":[193,208],"substantially":[194],"from":[196],"technical":[198],"conditions.":[199],"Comparisons":[200],"other":[202],"methods":[203],"used":[204],"literature":[207],"provided.":[209],"Analyses":[210],"real-life":[212],"included.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2016-06-24T00:00:00"}