{"id":"https://openalex.org/W3215122718","doi":"https://doi.org/10.1080/03610918.2021.2005096","title":"Scalable kernel two-sample tests via empirical likelihood and jackknife","display_name":"Scalable kernel two-sample tests via empirical likelihood and jackknife","publication_year":2021,"publication_date":"2021-11-22","ids":{"openalex":"https://openalex.org/W3215122718","doi":"https://doi.org/10.1080/03610918.2021.2005096","mag":"3215122718"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.2005096","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.2005096","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-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/A5104013356","display_name":"Qian Wen","orcid":"https://orcid.org/0000-0002-7516-1664"},"institutions":[{"id":"https://openalex.org/I57328836","display_name":"North Dakota State University","ror":"https://ror.org/05h1bnb22","country_code":"US","type":"education","lineage":["https://openalex.org/I57328836"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qian Wen","raw_affiliation_strings":["Department of Statistics, North Dakota State University, Fargo, ND, USA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Dakota State University, Fargo, ND, USA","institution_ids":["https://openalex.org/I57328836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069183609","display_name":"Mingao Yuan","orcid":"https://orcid.org/0000-0002-3962-262X"},"institutions":[{"id":"https://openalex.org/I57328836","display_name":"North Dakota State University","ror":"https://ror.org/05h1bnb22","country_code":"US","type":"education","lineage":["https://openalex.org/I57328836"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mingao Yuan","raw_affiliation_strings":["Department of Statistics, North Dakota State University, Fargo, ND, USA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Dakota State University, Fargo, ND, USA","institution_ids":["https://openalex.org/I57328836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069183609"],"corresponding_institution_ids":["https://openalex.org/I57328836"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.16682389,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"52","issue":"12","first_page":"5975","last_page":"5990"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9994000196456909,"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":0.9994000196456909,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9959999918937683,"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/jackknife-resampling","display_name":"Jackknife resampling","score":0.9166401624679565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6911230683326721},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6249817609786987},{"id":"https://openalex.org/keywords/null-hypothesis","display_name":"Null hypothesis","score":0.6108652353286743},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.5901170969009399},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.5854178071022034},{"id":"https://openalex.org/keywords/null-distribution","display_name":"Null distribution","score":0.5252439975738525},{"id":"https://openalex.org/keywords/null","display_name":"Null (SQL)","score":0.518439531326294},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.4813588261604309},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.4773830771446228},{"id":"https://openalex.org/keywords/chi-square-test","display_name":"Chi-square test","score":0.46408697962760925},{"id":"https://openalex.org/keywords/empirical-likelihood","display_name":"Empirical likelihood","score":0.45738595724105835},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.42622271180152893},{"id":"https://openalex.org/keywords/empirical-distribution-function","display_name":"Empirical distribution function","score":0.421773225069046},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4194403290748596},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4169851243495941},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36752474308013916},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.26002025604248047},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.22447121143341064},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1159539520740509},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.09928742051124573}],"concepts":[{"id":"https://openalex.org/C81790035","wikidata":"https://www.wikidata.org/wiki/Q847158","display_name":"Jackknife resampling","level":3,"score":0.9166401624679565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6911230683326721},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6249817609786987},{"id":"https://openalex.org/C191988596","wikidata":"https://www.wikidata.org/wiki/Q628374","display_name":"Null hypothesis","level":2,"score":0.6108652353286743},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.5901170969009399},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.5854178071022034},{"id":"https://openalex.org/C120639","wikidata":"https://www.wikidata.org/wiki/Q7068958","display_name":"Null distribution","level":4,"score":0.5252439975738525},{"id":"https://openalex.org/C203763787","wikidata":"https://www.wikidata.org/wiki/Q371029","display_name":"Null (SQL)","level":2,"score":0.518439531326294},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.4813588261604309},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.4773830771446228},{"id":"https://openalex.org/C32809988","wikidata":"https://www.wikidata.org/wiki/Q1071004","display_name":"Chi-square test","level":2,"score":0.46408697962760925},{"id":"https://openalex.org/C2781117939","wikidata":"https://www.wikidata.org/wiki/Q5374245","display_name":"Empirical likelihood","level":3,"score":0.45738595724105835},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42622271180152893},{"id":"https://openalex.org/C98385598","wikidata":"https://www.wikidata.org/wiki/Q1339385","display_name":"Empirical distribution function","level":2,"score":0.421773225069046},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4194403290748596},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4169851243495941},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36752474308013916},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.26002025604248047},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.22447121143341064},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1159539520740509},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.09928742051124573},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2021.2005096","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.2005096","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W199271301","https://openalex.org/W1851612831","https://openalex.org/W1973595294","https://openalex.org/W1974234363","https://openalex.org/W1974971448","https://openalex.org/W1992068214","https://openalex.org/W2005797855","https://openalex.org/W2027390033","https://openalex.org/W2030150661","https://openalex.org/W2062541416","https://openalex.org/W2067707835","https://openalex.org/W2094514589","https://openalex.org/W2100258006","https://openalex.org/W2116855184","https://openalex.org/W2122093723","https://openalex.org/W2125865219","https://openalex.org/W2140142253","https://openalex.org/W2164943005","https://openalex.org/W2183704914","https://openalex.org/W2212660284","https://openalex.org/W2418306335","https://openalex.org/W2516618844","https://openalex.org/W2890145236","https://openalex.org/W2924199702","https://openalex.org/W2963051744","https://openalex.org/W3183522126","https://openalex.org/W6608099645","https://openalex.org/W6678814708","https://openalex.org/W6688325169"],"related_works":["https://openalex.org/W2085201310","https://openalex.org/W2294933899","https://openalex.org/W2126013548","https://openalex.org/W2068394532","https://openalex.org/W1975288153","https://openalex.org/W2965709432","https://openalex.org/W2203011077","https://openalex.org/W2845770748","https://openalex.org/W1779117663","https://openalex.org/W2890145236"],"abstract_inverted_index":{"Two-sample":[0],"hypothesis":[1,81,151],"testing":[2,17,94],"is":[3,18,23,67],"a":[4,130,179],"classical":[5],"problem":[6],"in":[7,136,190],"statistics.":[8],"Given":[9],"i.i.d.":[10],"samples":[11],"X1,\u2026,Xm\u223cP":[12],"and":[13,99,109,121,171,198],"Y1,\u2026,Yn\u223cQ,":[14],"the":[15,31,35,79,105,116,139,149,164,185,204],"two-sample":[16,165],"to":[19,43,48,128,138,155,162,177,192],"test":[20,131,181],"whether":[21],"P":[22],"significantly":[24],"different":[25],"from":[26],"Q":[27],"or":[28],"not.":[29],"Among":[30],"various":[32],"statistical":[33],"tests,":[34],"maximum":[36],"mean":[37],"discrepancy":[38],"(MMD)":[39],"has":[40],"been":[41],"proven":[42],"have":[44],"excellent":[45],"performance":[46],"compared":[47],"traditional":[49],"methods.":[50],"In":[51,88],"application":[52,201],"of":[53,144],"MMD,":[54],"we":[55,91,160],"are":[56,107],"confronted":[57],"with":[58,70,142],"two":[59,93],"challenges:":[60],"(I)":[61,98],"For":[62],"large":[63],"data":[64,106,200],"set,":[65],"MMD":[66,117],"computationally":[68],"demanding":[69],"computational":[71],"cost":[72],"O((m+n)2);":[73],"(II)":[74,100],"The":[75,196],"limiting":[76],"distribution":[77,137,141,191],"under":[78,148],"null":[80,150,186],"usually":[82],"contains":[83],"infinitely":[84],"many":[85],"unknown":[86],"parameters.":[87],"this":[89],"paper,":[90],"propose":[92,161],"methods":[95],"that":[96,203],"solve":[97],"respectively.":[101],"To":[102,157],"address":[103,158],"(I),":[104],"independently":[108],"evenly":[110],"divided":[111],"into":[112],"k":[113,153],"groups,":[114],"calculate":[115],"on":[118,168],"each":[119,169],"group":[120,170],"then":[122],"combine":[123,172],"them":[124,173],"by":[125,174],"empirical":[126,175],"likelihood":[127,176],"yield":[129],"statistic":[132,182],"Tk.":[133],"It":[134],"converges":[135,189],"chi-square":[140],"degree":[143],"freedom":[145],"one":[146],"(\u03c712)":[147],"as":[152],"goes":[154],"infinity.":[156],"(II),":[159],"compute":[163],"jackknife":[166],"pseudo-values":[167],"get":[178],"new":[180],"Rnm.":[183],"Under":[184],"hypothesis,":[187],"Rnm":[188],"\u03c712":[193],"if":[194],"k=o(n).":[195],"simulation":[197],"real":[199],"indicates":[202],"proposed":[205],"tests":[206],"performs":[207],"well.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}