{"id":"https://openalex.org/W2401265133","doi":"https://doi.org/10.1109/icassp.2016.7472497","title":"Confidence assessment for spectral estimation based on estimated covariances","display_name":"Confidence assessment for spectral estimation based on estimated covariances","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2401265133","doi":"https://doi.org/10.1109/icassp.2016.7472497","mag":"2401265133"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472497","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/A5065730184","display_name":"Johan Karlsson","orcid":"https://orcid.org/0009-0007-3575-6747"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Johan Karlsson","raw_affiliation_strings":["Department of Mathematics, KTH Royal Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, KTH Royal Institute of Technology","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018917817","display_name":"Per Enqvist","orcid":"https://orcid.org/0000-0002-4635-3202"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Per Enqvist","raw_affiliation_strings":["Department of Mathematics, KTH Royal Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, KTH Royal Institute of Technology","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051318304","display_name":"Ather Gattami","orcid":"https://orcid.org/0000-0003-4298-3634"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ather Gattami","raw_affiliation_strings":["Bitynamics Research Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Bitynamics Research Stockholm, Sweden","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065730184"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04031381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"4343","last_page":"4347"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9976999759674072,"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":0.9976999759674072,"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/T11716","display_name":"Random Matrices and Applications","score":0.9973999857902527,"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":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.6452124714851379},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5996712446212769},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5534053444862366},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.5525349974632263},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5525180101394653},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.49396616220474243},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4701293408870697},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.4531746506690979},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45210903882980347},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.44859328866004944},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3990468680858612},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3813786506652832}],"concepts":[{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.6452124714851379},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5996712446212769},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5534053444862366},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.5525349974632263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5525180101394653},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.49396616220474243},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4701293408870697},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.4531746506690979},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45210903882980347},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.44859328866004944},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3990468680858612},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3813786506652832},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472497","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/16","score":0.7900000214576721,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W45555743","https://openalex.org/W210359992","https://openalex.org/W615589970","https://openalex.org/W1494137514","https://openalex.org/W1496923911","https://openalex.org/W1506558619","https://openalex.org/W1586554030","https://openalex.org/W1999640431","https://openalex.org/W2037095848","https://openalex.org/W2039486687","https://openalex.org/W2040061802","https://openalex.org/W2091559329","https://openalex.org/W2097581234","https://openalex.org/W2107847257","https://openalex.org/W2126119581","https://openalex.org/W2153668078","https://openalex.org/W2165941627","https://openalex.org/W2168636192","https://openalex.org/W2168822076","https://openalex.org/W2763292376","https://openalex.org/W2964282423","https://openalex.org/W3098335816","https://openalex.org/W4238253035","https://openalex.org/W4242912218","https://openalex.org/W6629474112","https://openalex.org/W6629518394","https://openalex.org/W6639680759"],"related_works":["https://openalex.org/W2165749285","https://openalex.org/W2062626603","https://openalex.org/W2788344745","https://openalex.org/W2062336688","https://openalex.org/W2474979212","https://openalex.org/W2562455930","https://openalex.org/W2001036795","https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W2024369332"],"abstract_inverted_index":{"In":[0,35,70,91],"probability":[1],"theory,":[2],"time":[3],"series":[4],"analysis,":[5],"and":[6,11,27,49,81,106,118,121,143],"signal":[7],"processing,":[8],"many":[9],"identification":[10],"estimation":[12,33],"methods":[13],"rely":[14],"on":[15,51,78,100,112],"covariance":[16,79],"estimates":[17,80],"as":[18],"an":[19],"intermediate":[20],"statistics.":[21],"Errors":[22],"in":[23,37,138],"estimated":[24],"covariances":[25],"propagate":[26],"degrade":[28],"the":[29,32,46,58,61,108,122,136,145,150],"quality":[30],"of":[31,45,60,102],"result.":[34],"particular,":[36,92],"large":[38],"network":[39,47],"systems":[40],"where":[41],"each":[42],"system":[43],"node":[44],"gather":[48],"pass":[50],"results,":[52],"it":[53],"is":[54],"important":[55],"to":[56],"know":[57],"reliability":[59],"information":[62],"so":[63],"that":[64],"informed":[65],"decisions":[66],"can":[67,85],"be":[68,86],"made.":[69],"this":[71],"work,":[72],"we":[73,93,133],"design":[74],"confidence":[75,97,152],"regions":[76,98],"based":[77,99,111],"study":[82],"how":[83],"these":[84,131],"used":[87],"for":[88,127],"spectral":[89,147],"estimation.":[90],"consider":[94],"three":[95,113],"different":[96],"sets":[101],"unitarily":[103],"invariant":[104],"matrices":[105],"bound":[107,135,148],"eigenvalue":[109,125],"distribution":[110,126],"principles:":[114],"uniform":[115],"bounds;":[116],"arithmetic":[117],"harmonic":[119],"means;":[120],"Marcenko-Pastur":[123],"Law":[124],"random":[128],"matrices.":[129],"Using":[130],"methodologies":[132],"robustly":[134],"energy":[137],"a":[139],"selected":[140],"frequency":[141],"band,":[142],"compare":[144],"resulting":[146],"from":[149],"respective":[151],"regions.":[153]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
