{"id":"https://openalex.org/W2116254297","doi":"https://doi.org/10.1109/isit.2010.5513375","title":"On minimax robust detection of stationary Gaussian signals in white Gaussian noise","display_name":"On minimax robust detection of stationary Gaussian signals in white Gaussian noise","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2116254297","doi":"https://doi.org/10.1109/isit.2010.5513375","mag":"2116254297"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2010.5513375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2010.5513375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Symposium on Information Theory","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/A5100360030","display_name":"Wenyi Zhang","orcid":"https://orcid.org/0000-0003-4227-0749"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenyi Zhang","raw_affiliation_strings":["Department of EEIS, University of Science and Technology, China","Dept. of EEIS, University of Science and Technology of China, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of EEIS, University of Science and Technology, China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Dept. of EEIS, University of Science and Technology of China, China#TAB#","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042307561","display_name":"H. Vincent Poor","orcid":"https://orcid.org/0000-0002-2062-131X"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"H. Vincent Poor","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","Department of Electrical Engineering Princeton University NJ USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Department of Electrical Engineering Princeton University NJ USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100360030"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.3156,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70587134,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1628","last_page":"1632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9887999892234802,"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/minimax","display_name":"Minimax","score":0.7301226854324341},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7042189240455627},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.6402822732925415},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.5845576524734497},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5811807513237},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.5678818225860596},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5631980299949646},{"id":"https://openalex.org/keywords/convexity","display_name":"Convexity","score":0.528122067451477},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.5201687812805176},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5013606548309326},{"id":"https://openalex.org/keywords/noise-power","display_name":"Noise power","score":0.5001077651977539},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.465317964553833},{"id":"https://openalex.org/keywords/saddle-point","display_name":"Saddle point","score":0.43084293603897095},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4094792902469635},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37480098009109497},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3309609293937683},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.1854132115840912},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07414597272872925}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.7301226854324341},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7042189240455627},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.6402822732925415},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.5845576524734497},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5811807513237},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.5678818225860596},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5631980299949646},{"id":"https://openalex.org/C72134830","wikidata":"https://www.wikidata.org/wiki/Q5166524","display_name":"Convexity","level":2,"score":0.528122067451477},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.5201687812805176},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5013606548309326},{"id":"https://openalex.org/C203234222","wikidata":"https://www.wikidata.org/wiki/Q2133519","display_name":"Noise power","level":3,"score":0.5001077651977539},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.465317964553833},{"id":"https://openalex.org/C2681867","wikidata":"https://www.wikidata.org/wiki/Q690935","display_name":"Saddle point","level":2,"score":0.43084293603897095},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4094792902469635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37480098009109497},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3309609293937683},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.1854132115840912},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07414597272872925},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isit.2010.5513375","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2010.5513375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Symposium on Information Theory","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.761.8473","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.761.8473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1004.5479.pdf","raw_type":"text"}],"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/W1534416612","https://openalex.org/W1590772317","https://openalex.org/W1603443343","https://openalex.org/W2006307866","https://openalex.org/W2014002306","https://openalex.org/W2042930170","https://openalex.org/W2045021643","https://openalex.org/W2045403874","https://openalex.org/W2049898120","https://openalex.org/W2096015213","https://openalex.org/W2116254297","https://openalex.org/W2128799971","https://openalex.org/W2130118845","https://openalex.org/W2132105090","https://openalex.org/W2136801871","https://openalex.org/W2143109198","https://openalex.org/W2146854872","https://openalex.org/W2154133052","https://openalex.org/W2158172508","https://openalex.org/W2164278741","https://openalex.org/W2172259820","https://openalex.org/W4243833160"],"related_works":["https://openalex.org/W1601883103","https://openalex.org/W2371289173","https://openalex.org/W3142490657","https://openalex.org/W2021476328","https://openalex.org/W2015579740","https://openalex.org/W2155687826","https://openalex.org/W3214387023","https://openalex.org/W2125609481","https://openalex.org/W2170302359","https://openalex.org/W2763384776"],"abstract_inverted_index":{"The":[0,28],"problem":[1,90],"of":[2,20,30,41,58,74],"detecting":[3],"a":[4,46,50,92,105],"wide-sense":[5],"stationary":[6],"Gaussian":[7,13],"signal":[8,22],"process":[9,23],"embedded":[10],"in":[11,111],"white":[12],"noise,":[14],"where":[15],"the":[16,21,37,42,56,59,71,84,87,99,112,119],"power":[17,108],"spectral":[18,75,109],"density":[19,76,110],"exhibits":[24],"uncertainty,":[25],"is":[26,34,68,80,96,122],"investigated.":[27],"performance":[29],"minimax":[31,89],"robust":[32],"detection":[33],"characterized":[35],"by":[36,98],"exponential":[38],"decay":[39],"rate":[40],"miss":[43],"probability":[44],"under":[45,83],"Neyman-Pearson":[47],"criterion":[48],"with":[49],"fixed":[51],"false":[52],"alarm":[53],"probability,":[54],"as":[55],"length":[57],"observation":[60],"interval":[61],"grows":[62],"without":[63],"bound.":[64],"A":[65],"dominance":[66,85],"condition":[67,117],"identified":[69],"for":[70],"uncertainty":[72,113,120],"set":[73,121],"functions,":[77],"and":[78],"it":[79],"established":[81],"that,":[82],"condition,":[86],"resulting":[88],"possesses":[91],"saddle":[93],"point,":[94],"which":[95],"achievable":[97],"likelihood":[100],"ratio":[101],"tests":[102],"matched":[103],"to":[104,124],"so-called":[106],"dominated":[107],"set.":[114],"No":[115],"convexity":[116],"on":[118],"required":[123],"establish":[125],"this":[126],"result.":[127]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
