{"id":"https://openalex.org/W2402381165","doi":"https://doi.org/10.1109/icassp.2016.7472161","title":"Estimating high-dimensional covariance matrices with misses for Kronecker product expansion models","display_name":"Estimating high-dimensional covariance matrices with misses for Kronecker product expansion models","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2402381165","doi":"https://doi.org/10.1109/icassp.2016.7472161","mag":"2402381165"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5026423400","display_name":"Mahdi Zamanighomi","orcid":"https://orcid.org/0009-0009-2503-5545"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mahdi Zamanighomi","raw_affiliation_strings":["Department of Statistics, Stanford University, Stanford, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Stanford University, Stanford, CA, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009345756","display_name":"Zhengdao Wang","orcid":"https://orcid.org/0000-0003-2491-7879"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengdao Wang","raw_affiliation_strings":["Department of Elec. and Comp. Engr., Iowa State University, Ames, IA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Elec. and Comp. Engr., Iowa State University, Ames, IA, USA","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026758314","display_name":"Georgios B. Giannakis","orcid":"https://orcid.org/0000-0002-0196-0260"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgios B. Giannakis","raw_affiliation_strings":["Dept. of Elec. and Comp. Engr., University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Elec. and Comp. Engr., University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7249,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.7510263,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"55","issue":null,"first_page":"2667","last_page":"2671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9968000054359436,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9968000054359436,"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/T11716","display_name":"Random Matrices and Applications","score":0.996399998664856,"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":0.9937000274658203,"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/missing-data","display_name":"Missing data","score":0.8291426301002502},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6569755673408508},{"id":"https://openalex.org/keywords/kronecker-product","display_name":"Kronecker product","score":0.6064376831054688},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5799216032028198},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5645390748977661},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5417535901069641},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5281277298927307},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.5009734630584717},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43992874026298523},{"id":"https://openalex.org/keywords/kronecker-delta","display_name":"Kronecker delta","score":0.40976250171661377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35187071561813354},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3226798176765442},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31888169050216675}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8291426301002502},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6569755673408508},{"id":"https://openalex.org/C46030957","wikidata":"https://www.wikidata.org/wiki/Q1238125","display_name":"Kronecker product","level":3,"score":0.6064376831054688},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5799216032028198},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5645390748977661},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5417535901069641},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5281277298927307},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.5009734630584717},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43992874026298523},{"id":"https://openalex.org/C39482219","wikidata":"https://www.wikidata.org/wiki/Q192826","display_name":"Kronecker delta","level":2,"score":0.40976250171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35187071561813354},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3226798176765442},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31888169050216675},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W48439977","https://openalex.org/W1515333551","https://openalex.org/W1967925177","https://openalex.org/W1981131227","https://openalex.org/W1981297600","https://openalex.org/W2047165046","https://openalex.org/W2103193999","https://openalex.org/W2103972604","https://openalex.org/W2112556364","https://openalex.org/W2118550318","https://openalex.org/W2167167503","https://openalex.org/W2167200240","https://openalex.org/W2171118759","https://openalex.org/W2174160981","https://openalex.org/W2963893933","https://openalex.org/W3103475671","https://openalex.org/W4297090691","https://openalex.org/W6684589385"],"related_works":["https://openalex.org/W123417539","https://openalex.org/W2079407403","https://openalex.org/W2060299328","https://openalex.org/W2117336295","https://openalex.org/W4375957393","https://openalex.org/W4233239985","https://openalex.org/W3102722572","https://openalex.org/W2090103374","https://openalex.org/W577870507","https://openalex.org/W2077976955"],"abstract_inverted_index":{"We":[0,12,99,124],"study":[1],"the":[2,31,66,84,111,117,131,144],"problem":[3],"of":[4,20,33,68,79,120,134,140],"high-dimensional":[5],"covariance":[6,14,113],"matrix":[7,21,114],"estimation":[8,145],"from":[9],"partial":[10],"observations.":[11],"consider":[13],"matrices":[15],"modeled":[16],"as":[17],"Kronecker":[18],"products":[19],"factors,":[22],"and":[23,93,105,137],"rely":[24],"on":[25,130,143],"observations":[26],"with":[27],"missing":[28,34,69,80,87,141],"values.":[29],"In":[30,45],"absence":[32],"data,":[35],"observation":[36],"vectors":[37],"are":[38,149],"assumed":[39],"to":[40,57,65,75,110,156],"be":[41],"i.i.d":[42],"multivariate":[43],"Gaussian.":[44],"particular,":[46],"we":[47],"propose":[48],"a":[49,76,101,121,126],"new":[50],"procedure":[51],"computationally":[52],"affordable":[53],"in":[54,154],"high":[55],"dimension":[56],"extend":[58],"an":[59],"existing":[60],"permuted":[61],"rank-penalized":[62],"least-squares":[63],"method":[64],"case":[67],"data.":[70],"Our":[71],"approach":[72],"is":[73,89],"applicable":[74],"large":[77],"variety":[78],"data":[81],"mechanisms,":[82],"whether":[83],"process":[85],"generating":[86],"values":[88,142],"random":[90],"or":[91],"not,":[92],"does":[94],"not":[95],"require":[96],"imputation":[97],"techniques.":[98],"introduce":[100],"novel":[102],"unbiased":[103],"estimator":[104],"characterize":[106],"its":[107],"convergence":[108],"rate":[109],"true":[112],"measured":[115],"by":[116,151],"spectral":[118],"norm":[119],"permutation":[122],"operator.":[123],"establish":[125],"tight":[127],"outer":[128],"bound":[129],"square":[132],"error":[133],"our":[135,158],"estimate,":[136],"elucidate":[138],"consequences":[139],"performance.":[146],"Different":[147],"schemes":[148],"compared":[150],"numerical":[152],"simulations":[153],"order":[155],"test":[157],"proposed":[159],"estimator.":[160]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
