{"id":"https://openalex.org/W2150630477","doi":"https://doi.org/10.1109/icassp.2009.4960397","title":"A complex cross-spectral distribution model using Normal Variance Mean Mixtures","display_name":"A complex cross-spectral distribution model using Normal Variance Mean Mixtures","publication_year":2009,"publication_date":"2009-04-01","ids":{"openalex":"https://openalex.org/W2150630477","doi":"https://doi.org/10.1109/icassp.2009.4960397","mag":"2150630477"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2009.4960397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4960397","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","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/A5101406846","display_name":"Jason Palmer","orcid":"https://orcid.org/0000-0003-0468-289X"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"J. A. Palmer","raw_affiliation_strings":["Swartz Center of Computational Neuroscience, University of California, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"Swartz Center of Computational Neuroscience, University of California, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053345339","display_name":"Scott Makeig","orcid":"https://orcid.org/0000-0002-9048-8438"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. Makeig","raw_affiliation_strings":["Swartz Center of Computational Neuroscience, University of California, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"Swartz Center of Computational Neuroscience, University of California, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042298001","display_name":"Kenneth Kreutz-Delgado","orcid":"https://orcid.org/0000-0001-8135-6823"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K. Kreutz-Delgado","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101406846"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":0.3568,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60799788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3569","last_page":"3572"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983999729156494,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wishart-distribution","display_name":"Wishart distribution","score":0.7947716116905212},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5738815069198608},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5503366589546204},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.49668198823928833},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4696817994117737},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4500657320022583},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.4420516788959503},{"id":"https://openalex.org/keywords/normal-distribution","display_name":"Normal distribution","score":0.42516395449638367},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.4125359356403351},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.38335856795310974},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.2232382893562317},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.21152687072753906},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.08953571319580078}],"concepts":[{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.7947716116905212},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5738815069198608},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5503366589546204},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.49668198823928833},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4696817994117737},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4500657320022583},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.4420516788959503},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.42516395449638367},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.4125359356403351},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.38335856795310974},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.2232382893562317},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.21152687072753906},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.08953571319580078},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icassp.2009.4960397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4960397","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.295.661","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://sccn.ucsd.edu/~jason/icassp09.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.725.3576","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.725.3576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://dsp.ucsd.edu/%7Ekreutz/Publications/palmer2009complex.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.980.4726","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.980.4726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mirlab.org/conference_papers/International_Conference/ICASSP%202009/pdfs/0003569.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":11,"referenced_works":["https://openalex.org/W45374770","https://openalex.org/W1492352530","https://openalex.org/W2037095848","https://openalex.org/W2098301339","https://openalex.org/W2108214672","https://openalex.org/W2157765880","https://openalex.org/W2163899311","https://openalex.org/W2314640094","https://openalex.org/W3016175113","https://openalex.org/W4206469534","https://openalex.org/W6683344597"],"related_works":["https://openalex.org/W2146312983","https://openalex.org/W2389605595","https://openalex.org/W1586339758","https://openalex.org/W2046861201","https://openalex.org/W2048978945","https://openalex.org/W2099933086","https://openalex.org/W4249485354","https://openalex.org/W3099789143","https://openalex.org/W2371028619","https://openalex.org/W2589764326"],"abstract_inverted_index":{"We":[0,15],"propose":[1],"a":[2],"model":[3,19],"for":[4,31],"the":[5,22,27,32,42],"density":[6,20,25],"of":[7,26,39],"cross-spectral":[8,33],"coefficients":[9],"using":[10],"normal":[11],"variance":[12],"mean":[13],"mixtures.":[14],"show":[16],"that":[17],"this":[18],"generalizes":[21],"corresponding":[23],"marginal":[24],"complex":[28],"Wishart":[29],"distribution":[30,43],"density.":[34],"The":[35],"maximum":[36],"likelihood":[37],"estimate":[38],"parameters":[40],"in":[41,55],"is":[44],"derived,":[45],"and":[46],"examples":[47],"are":[48],"given":[49],"from":[50],"alpha":[51],"brain":[52],"wave":[53],"sources":[54],"separated":[56],"EEG":[57],"data.":[58]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
