{"id":"https://openalex.org/W3028354575","doi":"https://doi.org/10.1109/lsp.2020.2996060","title":"Improved Covariance Matrix Estimation With an Application in Portfolio Optimization","display_name":"Improved Covariance Matrix Estimation With an Application in Portfolio Optimization","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3028354575","doi":"https://doi.org/10.1109/lsp.2020.2996060","mag":"3028354575"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2020.2996060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.2996060","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5044144407","display_name":"Samruddhi Deshmukh","orcid":"https://orcid.org/0000-0003-3828-6561"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Samruddhi Deshmukh","raw_affiliation_strings":["Leonardo Machine Learning Division, SAP Labs, Bangalore, India","Needl.ai, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Leonardo Machine Learning Division, SAP Labs, Bangalore, India","institution_ids":[]},{"raw_affiliation_string":"Needl.ai, Bangalore, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048188079","display_name":"Amartansh Dubey","orcid":"https://orcid.org/0000-0002-1726-9802"},"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":"Amartansh Dubey","raw_affiliation_strings":["Department of Electronic and Computer Engineering, Hong Kong University of Science Technology, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Electronic and Computer Engineering, Hong Kong University of Science Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044144407"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6465,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.91198841,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"27","issue":null,"first_page":"985","last_page":"989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9925000071525574,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9916999936103821,"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/estimator","display_name":"Estimator","score":0.6949421167373657},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6558837890625},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.6518666744232178},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4769288897514343},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4725330173969269},{"id":"https://openalex.org/keywords/shrinkage-estimator","display_name":"Shrinkage estimator","score":0.468584269285202},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.46803075075149536},{"id":"https://openalex.org/keywords/scatter-matrix","display_name":"Scatter matrix","score":0.4639688730239868},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4560922682285309},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4443665146827698},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.43658170104026794},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.4304150342941284},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41655951738357544},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31560516357421875},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.22421032190322876}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6949421167373657},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6558837890625},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.6518666744232178},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4769288897514343},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4725330173969269},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.468584269285202},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.46803075075149536},{"id":"https://openalex.org/C176917957","wikidata":"https://www.wikidata.org/wiki/Q7430596","display_name":"Scatter matrix","level":4,"score":0.4639688730239868},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4560922682285309},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4443665146827698},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.43658170104026794},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.4304150342941284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41655951738357544},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31560516357421875},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.22421032190322876},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/lsp.2020.2996060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.2996060","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-129679","is_oa":false,"landing_page_url":"https://repository.hkust.edu.hk/ir/Record/1783.1-129679","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":"Conference paper"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-104382","is_oa":false,"landing_page_url":"http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=1070-9908&rft.volume=v. 27&rft.issue=&rft.date=2020&rft.spage=985&rft.aulast=Deshmukh&rft.aufirst=S.&rft.atitle=Improved+Covariance+Matrix+Estimation+with+an+Application+in+Portfolio+Optimization&rft.title=IEEE+Signal+Processing+Letters","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"},{"id":"pmh:oai:repository.ust.hk:1783.1-104382","is_oa":false,"landing_page_url":"http://repository.ust.hk/ir/Record/1783.1-104382","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":53,"referenced_works":["https://openalex.org/W62203920","https://openalex.org/W184583885","https://openalex.org/W1812558952","https://openalex.org/W1971672021","https://openalex.org/W1985787671","https://openalex.org/W1991209141","https://openalex.org/W2001876144","https://openalex.org/W2016538497","https://openalex.org/W2024860760","https://openalex.org/W2025907885","https://openalex.org/W2051108596","https://openalex.org/W2060581589","https://openalex.org/W2097998348","https://openalex.org/W2106084579","https://openalex.org/W2107993500","https://openalex.org/W2120060023","https://openalex.org/W2127152800","https://openalex.org/W2147855704","https://openalex.org/W2154844766","https://openalex.org/W2165408259","https://openalex.org/W2166604943","https://openalex.org/W2171485467","https://openalex.org/W2209388944","https://openalex.org/W2403802006","https://openalex.org/W2535502084","https://openalex.org/W2540953569","https://openalex.org/W2541919214","https://openalex.org/W2609219981","https://openalex.org/W2651855570","https://openalex.org/W2787596476","https://openalex.org/W2788438918","https://openalex.org/W2790122812","https://openalex.org/W2947345662","https://openalex.org/W2963474950","https://openalex.org/W2964163643","https://openalex.org/W2967294645","https://openalex.org/W2991696712","https://openalex.org/W3098994186","https://openalex.org/W3104215512","https://openalex.org/W3104363895","https://openalex.org/W3123612062","https://openalex.org/W3124158341","https://openalex.org/W3124834176","https://openalex.org/W3124979234","https://openalex.org/W3125814160","https://openalex.org/W3214123144","https://openalex.org/W4213099427","https://openalex.org/W6602535662","https://openalex.org/W6638364127","https://openalex.org/W6674385629","https://openalex.org/W6713783767","https://openalex.org/W6748833051","https://openalex.org/W6771116088"],"related_works":["https://openalex.org/W2032783896","https://openalex.org/W1982366353","https://openalex.org/W2341759200","https://openalex.org/W4299957430","https://openalex.org/W2739599042","https://openalex.org/W2964026106","https://openalex.org/W3038683520","https://openalex.org/W1990974373","https://openalex.org/W2973024663","https://openalex.org/W4286799712"],"abstract_inverted_index":{"One":[0],"of":[1,11,39,84,93,127,148,168],"the":[2,9,16,61,82,128,136,140,145,149],"major":[3],"challenges":[4],"in":[5,143,164],"multivariate":[6],"analysis":[7],"is":[8,49,107],"estimation":[10],"population":[12,154],"covariance":[13,18,23,94],"matrix":[14,19,24],"from":[15,33,125],"sample":[17],"(SCM).":[20],"Most":[21],"recent":[22],"estimators":[25,142],"use":[26],"either":[27],"shrinkage":[28,98],"transformations":[29],"or":[30],"asymptotic":[31],"results":[32],"Random":[34],"Matrix":[35],"Theory":[36],"(RMT).":[37],"Both":[38,64],"these":[40,85],"techniques":[41,86],"try":[42],"to":[43,50,57,59,114],"achieve":[44],"a":[45,101,108,165],"similar":[46],"goal":[47],"which":[48,80,111],"remove":[51],"noisy":[52],"correlations":[53],"and":[54,70,100,151,171],"add":[55],"structure":[56],"SCM":[58],"overcome":[60],"bias-variance":[62],"trade-off.":[63],"methods":[65],"have":[66],"their":[67],"respective":[68],"pros":[69],"cons.":[71],"In":[72],"this":[73],"paper,":[74],"we":[75,133],"propose":[76],"an":[77],"improved":[78],"estimator":[79,110,138,160],"exploits":[81],"advantages":[83],"by":[87,97,119],"taking":[88],"optimally":[89],"weighted":[90],"convex":[91],"combination":[92],"matrices":[95],"estimated":[96],"transformation":[99],"filter":[102],"based":[103],"on":[104],"RMT.":[105],"It":[106],"generalized":[109],"can":[112,161],"adapt":[113],"changing":[115],"sampling":[116],"noise":[117],"conditions":[118],"performing":[120],"hyperparameter":[121],"optimization.":[122],"Using":[123],"data":[124],"six":[126],"world's":[129],"biggest":[130],"stock":[131],"exchanges,":[132],"show":[134],"that":[135],"proposed":[137,159],"outperforms":[139],"existing":[141],"minimizing":[144],"out-of-sample":[146],"risk":[147],"portfolio":[150],"hence":[152],"predicts":[153],"statistics":[155],"more":[156],"precisely.":[157],"The":[158],"be":[162],"useful":[163],"wide":[166],"range":[167],"machine":[169],"learning":[170],"signal":[172],"processing":[173],"applications.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}