{"id":"https://openalex.org/W4220965280","doi":"https://doi.org/10.1080/10618600.2022.2051530","title":"Log-Rank-Type Tests for Equality of Distributions in High-Dimensional Spaces","display_name":"Log-Rank-Type Tests for Equality of Distributions in High-Dimensional Spaces","publication_year":2022,"publication_date":"2022-03-14","ids":{"openalex":"https://openalex.org/W4220965280","doi":"https://doi.org/10.1080/10618600.2022.2051530"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2051530","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2051530","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","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/A5101633114","display_name":"Linxi Liu","orcid":"https://orcid.org/0000-0001-7494-8417"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Linxi Liu","raw_affiliation_strings":["Department of Statistics, University of Pittsburgh, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Pittsburgh, Pittsburgh, PA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770785","display_name":"Meng Yang","orcid":"https://orcid.org/0000-0003-0366-9474"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Meng","raw_affiliation_strings":["Department of Statistics, Columbia University, New York, NY"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Columbia University, New York, NY","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113891310","display_name":"Xiaoru Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoru Wu","raw_affiliation_strings":["Facebook, Inc., Menlo Park, CA"],"affiliations":[{"raw_affiliation_string":"Facebook, Inc., Menlo Park, CA","institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I4210099336"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112872010","display_name":"Zhiliang Ying","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhiliang Ying","raw_affiliation_strings":["Department of Statistics, Columbia University, New York, NY"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Columbia University, New York, NY","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044625906","display_name":"Tian Zheng","orcid":"https://orcid.org/0000-0003-4889-0391"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tian Zheng","raw_affiliation_strings":["Department of Statistics, Columbia University, New York, NY"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Columbia University, New York, NY","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5112872010"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":0.6633,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73012637,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"31","issue":"4","first_page":"1384","last_page":"1396"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994999766349792,"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":0.9994999766349792,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9991999864578247,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9970999956130981,"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/resampling","display_name":"Resampling","score":0.7251541614532471},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7080917358398438},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5903517007827759},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5309675931930542},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.4822333753108978},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.45652735233306885},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.4482295513153076},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4471985995769501},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4193836748600006},{"id":"https://openalex.org/keywords/score","display_name":"Score","score":0.4116045832633972},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37262046337127686},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.23972788453102112},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.22158977389335632}],"concepts":[{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7251541614532471},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7080917358398438},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5903517007827759},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5309675931930542},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.4822333753108978},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.45652735233306885},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.4482295513153076},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4471985995769501},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4193836748600006},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.4116045832633972},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37262046337127686},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.23972788453102112},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.22158977389335632}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2022.2051530","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2051530","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1969875009","https://openalex.org/W1971232747","https://openalex.org/W1973595294","https://openalex.org/W1985732394","https://openalex.org/W1986717726","https://openalex.org/W2010353172","https://openalex.org/W2012053212","https://openalex.org/W2027390033","https://openalex.org/W2028331288","https://openalex.org/W2031330193","https://openalex.org/W2103485732","https://openalex.org/W2109694492","https://openalex.org/W2111527197","https://openalex.org/W2113962581","https://openalex.org/W2132138475","https://openalex.org/W2142983023","https://openalex.org/W2143594378","https://openalex.org/W2150000105","https://openalex.org/W2187668060","https://openalex.org/W2240536162","https://openalex.org/W2344661967","https://openalex.org/W2908301906","https://openalex.org/W2952085197","https://openalex.org/W3146371922","https://openalex.org/W4246296003","https://openalex.org/W4251428492","https://openalex.org/W4292027009"],"related_works":["https://openalex.org/W2099378941","https://openalex.org/W2059402160","https://openalex.org/W1997747147","https://openalex.org/W2052927015","https://openalex.org/W4296826658","https://openalex.org/W4290692481","https://openalex.org/W4251996830","https://openalex.org/W2924932104","https://openalex.org/W2135894974","https://openalex.org/W4297915994"],"abstract_inverted_index":{"Motivated":[0],"by":[1,20,70,126,145],"applications":[2],"in":[3,34,48],"high-dimensional":[4,61],"settings,":[5],"we":[6,108],"propose":[7],"a":[8,22,81,94,129,139,158],"novel":[9],"approach":[10,135],"for":[11,114,120,166],"testing":[12],"the":[13,37,65,73,89,97,101,104,111,121],"equality":[14],"of":[15,24,40,141],"two":[16,152],"or":[17,128],"more":[18],"populations":[19],"constructing":[21],"class":[23,39],"intensity":[25],"centered":[26],"score":[27,76],"processes.":[28],"The":[29,51,133],"resulting":[30],"tests":[31],"are":[32,45,54,124,169],"analogous":[33],"spirit":[35],"to":[36,60,80,138,157],"well-known":[38],"weighted":[41],"log-rank":[42,75],"statistics":[43,53,123],"that":[44,72],"widely":[46],"used":[47],"survival":[49],"analysis.":[50],"test":[52,122],"nonparametric,":[55],"computationally":[56],"simple,":[57],"and":[58,92,103,155],"applicable":[59],"data.":[62],"We":[63],"establish":[64,110],"usual":[66],"large":[67],"sample":[68],"properties":[69],"showing":[71],"underlying":[74],"process":[77],"converges":[78],"weakly":[79],"Gaussian":[82],"random":[83],"field":[84],"with":[85,93],"zero":[86],"mean":[87],"under":[88,96],"null":[90],"hypothesis":[91],"drift":[95],"contiguous":[98],"alternatives.":[99],"For":[100],"Kolmogorov\u2013Smirnov-type":[102],"Cramer-von":[105],"Mises-type":[106],"statistics,":[107],"also":[109,156],"consistency":[112],"result":[113],"any":[115],"fixed":[116],"alternative.":[117],"Cutoff":[118],"points":[119],"obtained":[125],"permutations":[127],"simulation-based":[130],"resampling":[131],"method.":[132],"new":[134],"is":[136],"applied":[137],"study":[140],"brain":[142],"activation":[143],"measured":[144],"functional":[146],"magnetic":[147],"resonance":[148],"imaging":[149],"when":[150],"performing":[151],"linguistic":[153],"tasks":[154],"prostate":[159],"cancer":[160],"DNA":[161],"microarray":[162],"dataset.":[163],"Supplementary":[164],"materials":[165],"this":[167],"article":[168],"available":[170],"online.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}