{"id":"https://openalex.org/W2133107474","doi":null,"title":"CODA: high dimensional copula discriminant analysis","display_name":"CODA: high dimensional copula discriminant analysis","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2133107474","mag":"2133107474"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.800.4456","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.800.4456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume14/han13a/han13a.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5021528850","display_name":"Fang Han","orcid":"https://orcid.org/0000-0003-2996-5693","observed_orcids":["https://orcid.org/0000-0003-2996-5693"]},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fang Han","raw_affiliation_strings":["Department of Biostatistics, Johns Hopkins University, Baltimore, Md"],"raw_orcid":"https://orcid.org/0000-0003-2996-5693","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Johns Hopkins University, Baltimore, Md","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101595500","display_name":"Tuo Zhao","orcid":"https://orcid.org/0000-0002-1536-7302","observed_orcids":["https://orcid.org/0000-0002-1536-7302"]},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tuo Zhao","raw_affiliation_strings":["Department of Computer Science, Johns Hopkins University, Baltimore, MD;"],"raw_orcid":"https://orcid.org/0000-0002-1536-7302","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Johns Hopkins University, Baltimore, MD;","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100349057","display_name":"Han Liu","orcid":"https://orcid.org/0000-0002-9386-2464","observed_orcids":["https://orcid.org/0000-0002-9386-2464"]},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Han Liu","raw_affiliation_strings":["Department of Operations Research & Financial Engineering, Princeton University, Princeton, NJ#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-9386-2464","affiliations":[{"raw_affiliation_string":"Department of Operations Research & Financial Engineering, Princeton University, Princeton, NJ#TAB#","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.5586,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.98234327,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"14","issue":"1","first_page":"629","last_page":"671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9545999765396118,"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":0.9545999765396118,"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/T13141","display_name":"Statistical Methods and Applications","score":0.02850000001490116,"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":0.004600000102072954,"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-classification","display_name":"high-dimensional classification","score":0.8930000066757202},{"id":"https://openalex.org/keywords/gaussian-copula","display_name":"Gaussian copula","score":0.7680000066757202},{"id":"https://openalex.org/keywords/spearman-s-rank-correlation","display_name":"Spearman correlation","score":0.8859999775886536},{"id":"https://openalex.org/keywords/kendall-s-tau","display_name":"Kendall's tau","score":0.9990000128746033},{"id":"https://openalex.org/keywords/misclassification","display_name":"misclassification","score":0.8399999737739563},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"linear discriminant analysis","score":0.859000027179718}],"concepts":[{"id":"https://openalex.org/C3962253","wikidata":"https://www.wikidata.org/wiki/Q852466","display_name":"Coda","level":2,"score":0.8042641878128052},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7692180275917053},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5550270676612854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5380486845970154},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.5234396457672119},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4963849186897278},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.49051517248153687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4851992428302765},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.48478764295578003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48456016182899475},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4373437762260437},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4217081069946289},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4115789532661438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4027377963066101},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29175156354904175},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26852428913116455},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0740390419960022},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.800.4456","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.800.4456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume14/han13a/han13a.pdf","raw_type":"text"},{"id":"mag:2133107474","doi":null,"is_oa":false,"landing_page_url":"http://jmlr.csail.mit.edu/papers/volume14/han13a/han13a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W403935824","https://openalex.org/W1529372075","https://openalex.org/W1554944419","https://openalex.org/W1614659291","https://openalex.org/W1768730780","https://openalex.org/W1915008591","https://openalex.org/W1966681180","https://openalex.org/W1974105260","https://openalex.org/W1988645907","https://openalex.org/W1988694953","https://openalex.org/W1989727964","https://openalex.org/W2010549559","https://openalex.org/W2010824638","https://openalex.org/W2015696818","https://openalex.org/W2020925091","https://openalex.org/W2025533349","https://openalex.org/W2035633352","https://openalex.org/W2043919728","https://openalex.org/W2046365501","https://openalex.org/W2049701820","https://openalex.org/W2051605894","https://openalex.org/W2063978378","https://openalex.org/W2082830048","https://openalex.org/W2089572795","https://openalex.org/W2091154312","https://openalex.org/W2103383765","https://openalex.org/W2113575907","https://openalex.org/W2113878188","https://openalex.org/W2118250684","https://openalex.org/W2125156589","https://openalex.org/W2125255467","https://openalex.org/W2127300249","https://openalex.org/W2132555912","https://openalex.org/W2137499573","https://openalex.org/W2137892504","https://openalex.org/W2138550913","https://openalex.org/W2143030432","https://openalex.org/W2143423446","https://openalex.org/W2148005287","https://openalex.org/W2150002853","https://openalex.org/W2150940164","https://openalex.org/W2151608460","https://openalex.org/W2158267770","https://openalex.org/W2170866857","https://openalex.org/W2962826552","https://openalex.org/W3104038823","https://openalex.org/W4294541781"],"related_works":["https://openalex.org/W2760675112","https://openalex.org/W2470282464","https://openalex.org/W2412163499","https://openalex.org/W2260402051","https://openalex.org/W1967806355","https://openalex.org/W7097046959","https://openalex.org/W2991388743","https://openalex.org/W7098385087","https://openalex.org/W4293235520","https://openalex.org/W2110364722"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2],"high":[3,63,141],"dimensional":[4,142],"classification":[5],"method,":[6],"named":[7],"the":[8,16,22,28,44,49,59,69,73,81,85,92,107,112,121,130,139],"Copula":[9,25],"Discriminant":[10],"Analysis":[11],"(CODA).":[12],"The":[13],"CODA":[14,113,131],"generalizes":[15],"normal-based":[17,140],"linear":[18,143],"discriminant":[19,74,144],"analysis":[20],"to":[21,91],"larger":[23],"Gaus-sian":[24],"models":[26],"(or":[27],"nonparanormal)":[29],"as":[30],"proposed":[31],"by":[32,100,111],"Liu":[33],"et":[34],"al.":[35],"(2009).":[36],"To":[37],"simultaneously":[38],"achieve":[39],"estimation":[40],"efficiency":[41,117],"and":[42,52,84],"robustness,":[43],"nonparametric":[45],"rank-based":[46],"methods":[47],"including":[48],"Spearman\u2019s":[50],"rho":[51],"Kendall\u2019s":[53],"tau":[54],"are":[55,123],"exploited":[56],"in":[57],"estimating":[58],"covariance":[60],"matrix.":[61],"In":[62],"dimen-sional":[64],"settings,":[65],"we":[66],"prove":[67],"that":[68,106,129],"sparsity":[70],"pattern":[71],"of":[72],"features":[75],"can":[76,133],"be":[77,134],"consistently":[78],"recovered":[79],"with":[80],"parametric":[82],"rate,":[83],"expected":[86],"misclassification":[87],"error":[88],"is":[89,97],"consistent":[90],"Bayes":[93],"risk.":[94],"Our":[95],"theory":[96],"backed":[98],"up":[99],"careful":[101],"numerical":[102],"experiments,":[103],"which":[104],"show":[105],"extra":[108],"flexibility":[109],"gained":[110],"method":[114,132],"incurs":[115],"little":[116],"loss":[118],"even":[119],"when":[120],"data":[122],"truly":[124],"Gaussian.":[125],"These":[126],"results":[127],"suggest":[128],"an":[135],"alternative":[136],"choice":[137],"besides":[138],"analysis.":[145]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":8}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}