{"id":"https://openalex.org/W4221091040","doi":"https://doi.org/10.1007/s10994-022-06138-3","title":"High-dimensional correlation matrix estimation for general continuous data with Bagging technique","display_name":"High-dimensional correlation matrix estimation for general continuous data with Bagging technique","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W4221091040","doi":"https://doi.org/10.1007/s10994-022-06138-3"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-022-06138-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06138-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06138-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06138-3.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100774764","display_name":"Chaojie Wang","orcid":"https://orcid.org/0000-0002-7644-7621"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]},{"id":"https://openalex.org/I4210111628","display_name":"Affiliated Hospital of Jiangsu University","ror":"https://ror.org/028pgd321","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210111628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaojie Wang","raw_affiliation_strings":["The Fourth Affiliated Hospital of Jiangsu University, School of Mathematical Science, Jiangsu University, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Fourth Affiliated Hospital of Jiangsu University, School of Mathematical Science, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961","https://openalex.org/I4210111628"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100761227","display_name":"Jin Du","orcid":"https://orcid.org/0009-0006-0456-3988"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jin Du","raw_affiliation_strings":["Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, SAR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, SAR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060018541","display_name":"Xiaodan Fan","orcid":"https://orcid.org/0000-0002-2744-9030"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Xiaodan Fan","raw_affiliation_strings":["Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, SAR, China"],"raw_orcid":"https://orcid.org/0000-0002-2744-9030","affiliations":[{"raw_affiliation_string":"Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, SAR, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060018541"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.4315,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93493938,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"111","issue":"8","first_page":"2905","last_page":"2927"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.9995999932289124,"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/T11716","display_name":"Random Matrices and Applications","score":0.9995999932289124,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9986000061035156,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9957000017166138,"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/mathematics","display_name":"Mathematics","score":0.7930976748466492},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.7008861899375916},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6821233034133911},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.6586587429046631},{"id":"https://openalex.org/keywords/scatter-matrix","display_name":"Scatter matrix","score":0.6103543639183044},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5631203651428223},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5522819757461548},{"id":"https://openalex.org/keywords/positive-definiteness","display_name":"Positive definiteness","score":0.4622715413570404},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4484331011772156},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4397355914115906},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.43112125992774963},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4196981191635132},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.37711650133132935},{"id":"https://openalex.org/keywords/positive-definite-matrix","display_name":"Positive-definite matrix","score":0.3112694323062897},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.1996232271194458}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7930976748466492},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.7008861899375916},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6821233034133911},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.6586587429046631},{"id":"https://openalex.org/C176917957","wikidata":"https://www.wikidata.org/wiki/Q7430596","display_name":"Scatter matrix","level":4,"score":0.6103543639183044},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5631203651428223},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5522819757461548},{"id":"https://openalex.org/C2778265155","wikidata":"https://www.wikidata.org/wiki/Q7233276","display_name":"Positive definiteness","level":4,"score":0.4622715413570404},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4484331011772156},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4397355914115906},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.43112125992774963},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4196981191635132},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37711650133132935},{"id":"https://openalex.org/C49712288","wikidata":"https://www.wikidata.org/wiki/Q77601250","display_name":"Positive-definite matrix","level":3,"score":0.3112694323062897},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.1996232271194458},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-022-06138-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06138-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06138-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10994-022-06138-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-022-06138-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-022-06138-3.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1705102025","display_name":null,"funder_award_id":"5501190012","funder_id":"https://openalex.org/F4320321410","funder_display_name":"Jiangsu University"},{"id":"https://openalex.org/G5530805396","display_name":null,"funder_award_id":"4053357","funder_id":"https://openalex.org/F4320322942","funder_display_name":"Chinese University of Hong Kong"}],"funders":[{"id":"https://openalex.org/F4320321410","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30"},{"id":"https://openalex.org/F4320322942","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4221091040.pdf","grobid_xml":"https://content.openalex.org/works/W4221091040.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W216325278","https://openalex.org/W958993015","https://openalex.org/W1249696972","https://openalex.org/W1499509602","https://openalex.org/W1520752838","https://openalex.org/W1891298814","https://openalex.org/W1981638497","https://openalex.org/W1986899418","https://openalex.org/W2057535756","https://openalex.org/W2062125287","https://openalex.org/W2072207996","https://openalex.org/W2097413644","https://openalex.org/W2105502962","https://openalex.org/W2114755499","https://openalex.org/W2130351130","https://openalex.org/W2132555912","https://openalex.org/W2154725178","https://openalex.org/W2157820287","https://openalex.org/W2339801242","https://openalex.org/W2963070656","https://openalex.org/W2964012924","https://openalex.org/W2964213435","https://openalex.org/W2964311145","https://openalex.org/W3098365576","https://openalex.org/W3101788651","https://openalex.org/W4300699176","https://openalex.org/W6785244853","https://openalex.org/W6808111085"],"related_works":["https://openalex.org/W2955292144","https://openalex.org/W1530566801","https://openalex.org/W2192462133","https://openalex.org/W252612664","https://openalex.org/W2019515979","https://openalex.org/W1987368804","https://openalex.org/W1981092891","https://openalex.org/W1276687128","https://openalex.org/W3134381438","https://openalex.org/W2066420774"],"abstract_inverted_index":{"Abstract":[0],"High-dimensional":[1],"covariance":[2,19,37,49],"matrix":[3,20,50,67],"estimation":[4,55],"plays":[5],"a":[6,130,141],"central":[7],"role":[8],"in":[9,99],"multivariate":[10],"statistical":[11],"analysis.":[12],"It":[13],"is":[14,21,27,76,125,149],"well-known":[15],"that":[16,89,137],"the":[17,24,30,33,36,47,64,69,90,105,108,115,122],"sample":[18,25,48,65],"singular":[22],"when":[23,121],"size":[26],"smaller":[28],"than":[29,152],"dimension":[31,123],"of":[32,46,56,107,111,117],"variable,":[34],"but":[35],"estimate":[38],"must":[39],"be":[40],"positive-definite.":[41],"This":[42],"motivates":[43],"some":[44,83],"modifications":[45],"to":[51,135],"preserve":[52],"its":[53],"efficient":[54],"pairwise":[57],"covariance.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62,86],"modify":[63],"correlation":[66,113],"using":[68],"Bagging":[70,74,91,119],"technique.":[71],"The":[72],"proposed":[73],"estimator":[75,92,110,120],"flexible":[77],"for":[78],"general":[79],"continuous":[80],"data.":[81],"Under":[82],"mild":[84],"conditions,":[85],"show":[87],"theoretically":[88],"can":[93],"ensure":[94],"positive-definiteness":[95],"with":[96],"probability":[97],"one":[98],"finite":[100],"samples.":[101],"We":[102],"also":[103],"prove":[104],"consistency":[106,116],"bootstrap":[109],"Pearson":[112],"and":[114,129,146,148],"our":[118,138],"p":[124],"fixed.":[126],"Simulation":[127],"results":[128],"real":[131],"application":[132],"are":[133],"provided":[134],"demonstrate":[136],"method":[139],"strikes":[140],"better":[142],"balance":[143],"between":[144],"RMSE":[145],"likelihood,":[147],"more":[150],"robust,":[151],"other":[153],"existing":[154],"estimators.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2025-10-10T00:00:00"}
