{"id":"https://openalex.org/W2061129424","doi":"https://doi.org/10.1109/msp.2013.2267231","title":"Sparse Parametric Models for Robust Nonstationary Signal Analysis: Leveraging the Power of Sparse Regression","display_name":"Sparse Parametric Models for Robust Nonstationary Signal Analysis: Leveraging the Power of Sparse Regression","publication_year":2013,"publication_date":"2013-10-16","ids":{"openalex":"https://openalex.org/W2061129424","doi":"https://doi.org/10.1109/msp.2013.2267231","mag":"2061129424"},"language":"en","primary_location":{"id":"doi:10.1109/msp.2013.2267231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/msp.2013.2267231","pdf_url":null,"source":{"id":"https://openalex.org/S120977877","display_name":"IEEE Signal Processing Magazine","issn_l":"1053-5888","issn":["1053-5888","1558-0792"],"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 Magazine","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/A5055794224","display_name":"Daniele Angelosante","orcid":"https://orcid.org/0000-0001-6570-6256"},"institutions":[{"id":"https://openalex.org/I885143765","display_name":"ABB (Switzerland)","ror":"https://ror.org/00ks5vt51","country_code":"CH","type":"company","lineage":["https://openalex.org/I885143765"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Daniele Angelosante","raw_affiliation_strings":["Corporate research center, ABB, Baden, Switzerland","ABB Corp. Res. Center, Baden-Daettwil, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate research center, ABB, Baden, Switzerland","institution_ids":["https://openalex.org/I885143765"]},{"raw_affiliation_string":"ABB Corp. Res. Center, Baden-Daettwil, Switzerland","institution_ids":["https://openalex.org/I885143765"]}]},{"author_position":"middle","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":["University of Minnesota, USA","Dept. of Electr. & Comput. Eng, Univ. of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng, Univ. of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050186120","display_name":"Nicholas D. Sidiropoulos","orcid":"https://orcid.org/0000-0002-3385-7911"},"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":"Nicholas D. Sidiropoulos","raw_affiliation_strings":["University of Minnesota, USA","Dept. of Electr. & Comput. Eng, Univ. of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng, Univ. of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7732,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.9292078,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"30","issue":"6","first_page":"64","last_page":"73"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"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":0.9987999796867371,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.996399998664856,"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/computer-science","display_name":"Computer science","score":0.7067524790763855},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6813421249389648},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.6251038312911987},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5905290246009827},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5031225085258484},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4747774302959442},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4585667848587036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4521312415599823},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.444894939661026},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.4426823854446411},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4399877190589905},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.4299968183040619},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18144968152046204},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13605400919914246},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1173209547996521},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09967365860939026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067524790763855},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6813421249389648},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.6251038312911987},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5905290246009827},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5031225085258484},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4747774302959442},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4585667848587036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4521312415599823},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.444894939661026},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.4426823854446411},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4399877190589905},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.4299968183040619},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18144968152046204},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13605400919914246},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1173209547996521},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09967365860939026},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/msp.2013.2267231","is_oa":false,"landing_page_url":"https://doi.org/10.1109/msp.2013.2267231","pdf_url":null,"source":{"id":"https://openalex.org/S120977877","display_name":"IEEE Signal Processing Magazine","issn_l":"1053-5888","issn":["1053-5888","1558-0792"],"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 Magazine","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4381165947","https://openalex.org/W170655188","https://openalex.org/W2900580051","https://openalex.org/W1993775099","https://openalex.org/W2422218765","https://openalex.org/W2120379204","https://openalex.org/W2279513187","https://openalex.org/W3126234533","https://openalex.org/W2313410437","https://openalex.org/W2104157383"],"abstract_inverted_index":{"Recent":[0],"research":[1,119],"and":[2,10,26,33,52,144,159,163,180],"experimental":[3],"findings,":[4],"as":[5,7,134],"well":[6],"technological":[8],"development":[9],"commercialization":[11],"efforts,":[12],"suggest":[13],"that":[14,127],"even":[15],"a":[16,81],"modest":[17],"amount":[18],"of":[19,123,136,155,189,194,204],"data":[20],"can":[21],"deliver":[22],"superior":[23],"signal":[24,38,45,51,67,137,169,210],"modeling":[25],"reconstruction":[27,54],"performance":[28],"if":[29],"sparsity":[30,129,195],"is":[31],"present":[32],"accounted":[34],"for.":[35],"Early":[36],"sparsity-aware":[37,62,156,161],"processing":[39,122,170],"techniques":[40,93],"have":[41,69],"mostly":[42],"targeted":[43],"stationary":[44],"analysis":[46,68,158],"using":[47],"offline":[48],"algorithms":[49],"for":[50,65,84,94,117,196,208],"image":[53],"from":[55,98],"Fourier":[56],"samples.":[57],"On":[58],"the":[59,114,152,168,187,191,197],"other":[60],"hand,":[61],"time-frequency":[63,90],"tools":[64],"nonstationary":[66,124,164,209],"recently":[70],"received":[71],"growing":[72],"attention.":[73],"In":[74],"this":[75],"context,":[76],"sparse":[77],"regression":[78],"has":[79],"offered":[80],"new":[82],"paradigm":[83],"instantaneous":[85],"frequency":[86],"estimation,":[87,143],"over":[88],"classical":[89],"representations.":[91],"Standard":[92],"estimating":[95],"model":[96,111],"parameters":[97],"time":[99],"series":[100],"yield":[101],"erroneous":[102],"fits":[103],"when,":[104],"e.g.,":[105],"abrupt":[106],"changes":[107],"or":[108],"outliers":[109],"cause":[110],"mismatches.":[112],"Accordingly,":[113],"need":[115],"arises":[116],"basic":[118],"in":[120,176],"robust":[121,141,162,205],"parametric":[125,165,206],"models":[126,166,207],"leverage":[128],"to":[130,167,200],"accomplish":[131],"tasks":[132],"such":[133],"tracking":[135],"variations,":[138],"outlier":[139],"rejection,":[140],"parameter":[142],"change":[145],"detection.":[146],"This":[147],"article":[148],"aims":[149],"at":[150],"delineating":[151],"analytical":[153],"background":[154],"time-series":[157],"introducing":[160],"readership,":[171],"through":[172],"readily":[173],"appreciated":[174],"applications":[175],"frequency-hopping":[177],"(FH)":[178],"communications":[179],"speech":[181],"compression.":[182],"Preliminary":[183],"results":[184],"strongly":[185],"support":[186],"vision":[188],"seeking":[190],"right":[192,198],"form":[193],"application":[199],"enable":[201],"sparsity-cognizant":[202],"estimation":[203],"analysis.":[211]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}