{"id":"https://openalex.org/W3007211411","doi":"https://doi.org/10.1109/isspit47144.2019.9001877","title":"Sensitivity of the General Linear Model to noise assumptions","display_name":"Sensitivity of the General Linear Model to noise assumptions","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007211411","doi":"https://doi.org/10.1109/isspit47144.2019.9001877","mag":"3007211411"},"language":"en","primary_location":{"id":"doi:10.1109/isspit47144.2019.9001877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspit47144.2019.9001877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","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/A5078466310","display_name":"Sujoy Chakraborty","orcid":"https://orcid.org/0000-0001-8233-0086"},"institutions":[{"id":"https://openalex.org/I24010308","display_name":"Stockton University","ror":"https://ror.org/0442n1j98","country_code":"US","type":"education","lineage":["https://openalex.org/I24010308"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sujoy Chakraborty","raw_affiliation_strings":["Dept of Computer Science, Stockton Uinversity, Galloway, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Dept of Computer Science, Stockton Uinversity, Galloway, NJ, USA","institution_ids":["https://openalex.org/I24010308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455113","display_name":"Hang Li","orcid":"https://orcid.org/0000-0001-5444-3382"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Li","raw_affiliation_strings":["Dept. of Electrical Engineering, Binghamton University, Binghamton, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Binghamton University, Binghamton, USA","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112857368","display_name":"Mark L. Fowler","orcid":null},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Fowler","raw_affiliation_strings":["Dept. of Electrical Engineering, Binghamton University, Binghamton, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Binghamton University, Binghamton, USA","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078466310"],"corresponding_institution_ids":["https://openalex.org/I24010308"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21911234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11236","display_name":"Control Systems and Identification","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9955000281333923,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/estimator","display_name":"Estimator","score":0.7595521211624146},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.7077263593673706},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.6323111653327942},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.6317187547683716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6008740067481995},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5463013648986816},{"id":"https://openalex.org/keywords/colors-of-noise","display_name":"Colors of noise","score":0.5460971593856812},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5055606365203857},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.49766066670417786},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.44478660821914673},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.4211188852787018},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.42012277245521545},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.36440926790237427},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3448660671710968},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.3408026695251465},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.31049448251724243},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2971232533454895},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.2008330523967743},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1296667456626892},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.11719608306884766},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09186872839927673}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7595521211624146},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.7077263593673706},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.6323111653327942},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.6317187547683716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6008740067481995},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5463013648986816},{"id":"https://openalex.org/C114996537","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Colors of noise","level":3,"score":0.5460971593856812},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5055606365203857},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49766066670417786},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.44478660821914673},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.4211188852787018},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.42012277245521545},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36440926790237427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3448660671710968},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3408026695251465},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31049448251724243},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2971232533454895},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2008330523967743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1296667456626892},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.11719608306884766},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09186872839927673},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/isspit47144.2019.9001877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspit47144.2019.9001877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W250076511","https://openalex.org/W1479979375","https://openalex.org/W1546617100","https://openalex.org/W1584444527","https://openalex.org/W1586030997","https://openalex.org/W1587174137","https://openalex.org/W1965392255","https://openalex.org/W1980387981","https://openalex.org/W2048100209","https://openalex.org/W2056340321","https://openalex.org/W2075302537","https://openalex.org/W2077305791","https://openalex.org/W2108153296","https://openalex.org/W2150112917","https://openalex.org/W2152976633","https://openalex.org/W2620920936","https://openalex.org/W3133603318","https://openalex.org/W4255783720","https://openalex.org/W4300454356","https://openalex.org/W6628836592","https://openalex.org/W6634927326","https://openalex.org/W6738582525"],"related_works":["https://openalex.org/W1700578922","https://openalex.org/W2079755857","https://openalex.org/W2116566913","https://openalex.org/W2810224748","https://openalex.org/W2170781407","https://openalex.org/W1986743941","https://openalex.org/W2588855097","https://openalex.org/W2341643946","https://openalex.org/W2092661960","https://openalex.org/W2811019791"],"abstract_inverted_index":{"For":[0],"signal":[1],"estimation":[2,139],"problems,":[3],"the":[4,13,16,40,52,63,74,84,87,104,117,131,135,148,151],"General":[5],"Linear":[6],"Model":[7],"has":[8],"been":[9],"developed":[10],"based":[11],"on":[12,130],"assumption":[14,158],"that":[15,34,51,116],"associated":[17],"noise":[18,29,53,71,98,113],"is":[19,44,54],"Gaussian":[20,157],"and":[21,126,159],"with":[22,69,93,110],"a":[23,80],"known":[24],"covariance":[25],"matrix.":[26],"With":[27],"this":[28,66,107,122],"assumption,":[30],"we":[31,48,77,101,128],"already":[32],"know":[33],"there":[35],"exists":[36],"an":[37],"estimator":[38,68,89,109,153],"of":[39,65,83,86,106,134,137,150],"underlying":[41],"parameters":[42],"which":[43],"efficient.":[45],"In":[46,73],"practice,":[47],"often":[49],"assume":[50],"white.":[55],"However,":[56],"something":[57],"interesting":[58],"to":[59,146],"observe":[60],"would":[61],"be":[62],"behavior":[64,149],"optimal":[67,88,108,152],"non-Gaussian":[70,96],"assumptions.":[72,99,114,142],"present":[75],"article,":[76],"have":[78,102],"shown":[79],"comparative":[81],"analysis":[82],"performance":[85,105],"for":[90],"linear":[91],"model":[92],"three":[94],"different":[95,111,141],"white":[97],"Also,":[100],"studied":[103],"colored":[112],"Given":[115],"theoretical":[118],"concepts":[119],"presented":[120],"in":[121],"paper":[123],"are":[124],"classical":[125,138],"well-studied,":[127],"stress":[129],"experimental":[132],"findings":[133],"theory":[136],"under":[140],"The":[143],"objective":[144],"was":[145],"study":[147],"under:":[154],"deviation":[155,160],"from":[156,161],"whiteness.":[162]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
