{"id":"https://openalex.org/W2520377505","doi":"https://doi.org/10.1109/sam.2016.7569684","title":"On the characterization, generation, and efficient estimation of the complex multivariate GGD","display_name":"On the characterization, generation, and efficient estimation of the complex multivariate GGD","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2520377505","doi":"https://doi.org/10.1109/sam.2016.7569684","mag":"2520377505"},"language":"en","primary_location":{"id":"doi:10.1109/sam.2016.7569684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2016.7569684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5091833364","display_name":"Rami Mowakeaa","orcid":"https://orcid.org/0000-0002-4256-3392"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rami Mowakeaa","raw_affiliation_strings":["University of Maryland, Baltimore, Maryland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006915006","display_name":"Zois Boukouvalas","orcid":"https://orcid.org/0000-0002-5131-1891"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zois Boukouvalas","raw_affiliation_strings":["University of Maryland, Baltimore, Maryland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060798483","display_name":"T\u00fclay Adal\u0131","orcid":"https://orcid.org/0000-0003-0594-2796"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tulay Adali","raw_affiliation_strings":["University of Maryland, Baltimore, Maryland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061118449","display_name":"Charles C. Cavalcante","orcid":"https://orcid.org/0000-0002-4198-4064"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Charles Cavalcante","raw_affiliation_strings":["Federal University of Cear\u00e1, Fortaleza, CE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Cear\u00e1, Fortaleza, CE","institution_ids":["https://openalex.org/I243754102"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7572,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72562554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"abs 1005 5170","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.998199999332428,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.998199999332428,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11236","display_name":"Control Systems and Identification","score":0.9915000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7370668649673462},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6888940930366516},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.678360641002655},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.6179882287979126},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6109558939933777},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5845391750335693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5599758625030518},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5579217672348022},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5548398494720459},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5107412338256836},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4955710172653198},{"id":"https://openalex.org/keywords/robust-statistics","display_name":"Robust statistics","score":0.4635466933250427},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.4515770971775055},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.44096431136131287},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4133206009864807},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37833279371261597},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3276973366737366},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.26908135414123535},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23108530044555664},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17947322130203247},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07058209180831909}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7370668649673462},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6888940930366516},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.678360641002655},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.6179882287979126},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6109558939933777},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5845391750335693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5599758625030518},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5579217672348022},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5548398494720459},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5107412338256836},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4955710172653198},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.4635466933250427},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.4515770971775055},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.44096431136131287},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4133206009864807},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37833279371261597},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3276973366737366},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26908135414123535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23108530044555664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17947322130203247},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07058209180831909},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sam.2016.7569684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2016.7569684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W639989723","https://openalex.org/W1630786939","https://openalex.org/W1794533974","https://openalex.org/W1988780137","https://openalex.org/W1991840148","https://openalex.org/W1992511687","https://openalex.org/W2027017271","https://openalex.org/W2040985763","https://openalex.org/W2058693698","https://openalex.org/W2069820356","https://openalex.org/W2078585350","https://openalex.org/W2081825416","https://openalex.org/W2115874509","https://openalex.org/W2140638323","https://openalex.org/W2159071216","https://openalex.org/W2170257405","https://openalex.org/W2209224425","https://openalex.org/W2294288691","https://openalex.org/W2300712633","https://openalex.org/W4211251910","https://openalex.org/W4256431090","https://openalex.org/W6638501081","https://openalex.org/W6685397067"],"related_works":["https://openalex.org/W2893341095","https://openalex.org/W2090488344","https://openalex.org/W4253991054","https://openalex.org/W2139005408","https://openalex.org/W4241043257","https://openalex.org/W2348053484","https://openalex.org/W2980167385","https://openalex.org/W2124594076","https://openalex.org/W2390882533","https://openalex.org/W2101014564"],"abstract_inverted_index":{"The":[0],"complex":[1,37,80],"multivariate":[2,33],"generalized":[3],"Gaussian":[4],"distribution":[5,11],"(CMGGD)":[6],"is":[7,23,97],"a":[8,14,85],"flexible":[9],"parametrized":[10],"suitable":[12],"for":[13,47,74,88,105,133],"variety":[15],"of":[16,61,91,93],"applications.":[17],"Previous":[18],"work":[19],"in":[20,31,100,130],"this":[21,65],"area":[22],"either":[24],"limited":[25],"to":[26,40],"the":[27,32,36,58,69,89,94,106],"univariate":[28],"case":[29],"or,":[30],"case,":[34],"restricts":[35],"vectors,":[38],"unjustifiably,":[39],"be":[41],"circular.":[42],"In":[43,64],"both":[44,98],"cases,":[45],"algorithms":[46,116],"parameter":[48,109,121],"estimation":[49,90],"also":[50],"suffer":[51],"from":[52],"convergence":[53,102],"or":[54],"accuracy":[55],"limitations":[56],"over":[57],"complete":[59,107],"range":[60],"their":[62],"parameters.":[63],"work,":[66],"we":[67],"develop":[68,84],"probability":[70],"density":[71],"function":[72],"(PDF)":[73],"CMGGD":[75,95],"that":[76,96],"properly":[77],"describes":[78],"noncircular":[79,134],"data.":[81,135],"We":[82,111],"then":[83],"fixed-point":[86],"algorithm":[87],"parameters":[92],"rapid":[99],"its":[101],"and":[103,122,125,128],"accurate":[104],"shape":[108,120],"range.":[110],"quantify":[112],"performance":[113],"against":[114],"other":[115],"while":[117],"varying":[118],"noncircularity,":[119],"data":[123],"dimensionality":[124],"demonstrate":[126],"robustness":[127],"gains":[129],"performance,":[131],"especially":[132]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
