{"id":"https://openalex.org/W4286579657","doi":"https://doi.org/10.1109/tsp.2022.3193232","title":"Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations","display_name":"Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4286579657","doi":"https://doi.org/10.1109/tsp.2022.3193232"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3193232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3193232","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","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/A5103131918","display_name":"Rui Zhou","orcid":"https://orcid.org/0000-0002-9463-0390"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhou","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9463-0390","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Shenzhen, China","institution_ids":["https://openalex.org/I4210099586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088040610","display_name":"Jiaxi Ying","orcid":"https://orcid.org/0000-0003-2102-6683"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jiaxi Ying","raw_affiliation_strings":["Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-2102-6683","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054606088","display_name":"Daniel P. Palomar","orcid":"https://orcid.org/0000-0001-5250-4874"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Daniel P. Palomar","raw_affiliation_strings":["Department of Electronic and Computer Engineering and Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Kowloon, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-5250-4874","affiliations":[{"raw_affiliation_string":"Department of Electronic and Computer Engineering and Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9928,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.84437437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"70","issue":null,"first_page":"4020","last_page":"4030"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9988999962806702,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9962999820709229,"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/covariance-matrix","display_name":"Covariance matrix","score":0.8139516115188599},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.8104394674301147},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6570013165473938},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6456904411315918},{"id":"https://openalex.org/keywords/scatter-matrix","display_name":"Scatter matrix","score":0.5433521866798401},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.5069577693939209},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.49280884861946106},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4901941418647766},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.48774605989456177},{"id":"https://openalex.org/keywords/rational-quadratic-covariance-function","display_name":"Rational quadratic covariance function","score":0.4519032835960388},{"id":"https://openalex.org/keywords/law-of-total-covariance","display_name":"Law of total covariance","score":0.449097216129303},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4133201241493225},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.383210688829422},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.36126309633255005},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36019229888916016},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.10721170902252197}],"concepts":[{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.8139516115188599},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.8104394674301147},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6570013165473938},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6456904411315918},{"id":"https://openalex.org/C176917957","wikidata":"https://www.wikidata.org/wiki/Q7430596","display_name":"Scatter matrix","level":4,"score":0.5433521866798401},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.5069577693939209},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.49280884861946106},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4901941418647766},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.48774605989456177},{"id":"https://openalex.org/C148893098","wikidata":"https://www.wikidata.org/wiki/Q7295778","display_name":"Rational quadratic covariance function","level":5,"score":0.4519032835960388},{"id":"https://openalex.org/C126372606","wikidata":"https://www.wikidata.org/wiki/Q6503511","display_name":"Law of total covariance","level":5,"score":0.449097216129303},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4133201241493225},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.383210688829422},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.36126309633255005},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36019229888916016},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10721170902252197},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2022.3193232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3193232","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-119771","is_oa":false,"landing_page_url":"http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000842058400005","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W22888870","https://openalex.org/W216325278","https://openalex.org/W1542938076","https://openalex.org/W1995436190","https://openalex.org/W1995834279","https://openalex.org/W2000769684","https://openalex.org/W2066306397","https://openalex.org/W2076818396","https://openalex.org/W2083665881","https://openalex.org/W2091560152","https://openalex.org/W2120248756","https://openalex.org/W2131242330","https://openalex.org/W2152851576","https://openalex.org/W2163852636","https://openalex.org/W2178935672","https://openalex.org/W2271854708","https://openalex.org/W2508393166","https://openalex.org/W2609219981","https://openalex.org/W2933407394","https://openalex.org/W2937507957","https://openalex.org/W2963055412","https://openalex.org/W2963421812","https://openalex.org/W2964108664","https://openalex.org/W3022601274","https://openalex.org/W3084069742","https://openalex.org/W3092006147","https://openalex.org/W3124158341","https://openalex.org/W3145687521","https://openalex.org/W4205293427","https://openalex.org/W4213251304","https://openalex.org/W4230148057","https://openalex.org/W4230661391","https://openalex.org/W4250589301","https://openalex.org/W4295827631","https://openalex.org/W4399585610","https://openalex.org/W6600917312","https://openalex.org/W6677509877","https://openalex.org/W6679560219","https://openalex.org/W6684954448","https://openalex.org/W6717018064","https://openalex.org/W6761781411"],"related_works":["https://openalex.org/W69681753","https://openalex.org/W4386993326","https://openalex.org/W2126916073","https://openalex.org/W2022823194","https://openalex.org/W341081156","https://openalex.org/W3211883524","https://openalex.org/W2164924639","https://openalex.org/W4283390736","https://openalex.org/W4390642134","https://openalex.org/W1989484292"],"abstract_inverted_index":{"Inferring":[0],"the":[1,23,36,55,60,89,99,107,132,153,162,196],"covariance":[2,32,64,100,133,149],"matrix":[3,33,65,72,101,134,150],"of":[4,8,48,57,102,116,184,191],"multivariate":[5],"data":[6,15,38,104,206],"is":[7,18,43,50,81,166,176],"great":[9],"interest":[10,117],"in":[11,87,118],"statistics,":[12],"finance,":[13],"and":[14,73,94,123,157,195,207],"science.":[16],"It":[17],"often":[19],"carried":[20],"out":[21],"via":[22],"maximum":[24],"likelihood":[25],"estimation":[26,90],"(MLE)":[27],"principle,":[28],"which":[29,111],"seeks":[30],"a":[31,63,70,74,82,114,171,181],"estimator":[34,42],"maximizing":[35],"observed":[37],"likelihood.":[39],"However,":[40],"such":[41],"usually":[44],"poor":[45],"when":[46],"number":[47,56],"samples":[49],"not":[51,127],"sufficiently":[52],"larger":[53],"than":[54],"variables.":[58],"With":[59],"assumption":[61],"that":[62,98],"can":[66],"be":[67],"decomposed":[68],"into":[69],"low-rank":[71,154],"diagonal":[75],"matrix,":[76],"factor":[77],"analysis":[78],"(FA)":[79],"model":[80,156],"popular":[83],"dimensionality":[84],"reduction":[85],"technique":[86],"improving":[88],"performance.":[91],"Recently,":[92],"more":[93,95],"evidence":[96],"shows":[97],"real-valued":[103],"may":[105],"admit":[106],"nonnegative":[108,158],"correlation":[109,159],"structure,":[110],"has":[112],"attracted":[113],"lot":[115],"some":[119],"areas":[120],"like":[121],"finance":[122],"psychometrics.":[124],"There":[125],"does":[126],"exist":[128],"any":[129],"work":[130],"estimating":[131],"simultaneously":[135],"satisfying":[136],"both":[137,204],"structures.":[138,160],"In":[139],"this":[140],"paper,":[141],"we":[142],"propose":[143],"an":[144,167],"MLE":[145],"problem":[146,164],"formulation":[147,165,194],"for":[148],"considering":[151],"jointly":[152],"FA":[155],"Since":[161],"proposed":[163,178,186,193],"intractable":[168],"non-convex":[169],"problem,":[170],"block":[172],"coordinate":[173],"descent":[174],"algorithm":[175,197],"further":[177],"to":[179],"solve":[180],"relaxed":[182],"version":[183],"our":[185,192],"formulation.":[187],"The":[188],"superior":[189],"performance":[190],"are":[198],"verified":[199],"through":[200],"numerical":[201],"simulations":[202],"on":[203],"synthetic":[205],"real":[208],"market":[209],"data.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}