{"id":"https://openalex.org/W1980129653","doi":"https://doi.org/10.1109/lsp.2012.2206583","title":"On the Exponentially Embedded Family (EEF) Rule for Model Order Selection","display_name":"On the Exponentially Embedded Family (EEF) Rule for Model Order Selection","publication_year":2012,"publication_date":"2012-06-28","ids":{"openalex":"https://openalex.org/W1980129653","doi":"https://doi.org/10.1109/lsp.2012.2206583","mag":"1980129653"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2012.2206583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2012.2206583","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Letters","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/A5007877889","display_name":"Petre Stoica","orcid":"https://orcid.org/0000-0002-7957-3711"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Petre Stoica","raw_affiliation_strings":["Department of Information Technology, University of Uppsala, Uppsala, Sweden","Department of Information Technology; Uppsala University; Uppsala Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, University of Uppsala, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]},{"raw_affiliation_string":"Department of Information Technology; Uppsala University; Uppsala Sweden","institution_ids":["https://openalex.org/I123387679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012466580","display_name":"Prabhu Babu","orcid":"https://orcid.org/0000-0002-9041-9010"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Prabhu Babu","raw_affiliation_strings":["Department of Information Technology, University of Uppsala, Uppsala, Sweden","Department of Information Technology; Uppsala University; Uppsala Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, University of Uppsala, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]},{"raw_affiliation_string":"Department of Information Technology; Uppsala University; Uppsala Sweden","institution_ids":["https://openalex.org/I123387679"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123387679"],"apc_list":null,"apc_paid":null,"fwci":1.3048,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.8274907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"19","issue":"9","first_page":"551","last_page":"554"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9995999932289124,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9943000078201294,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9639999866485596,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.8447777032852173},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.716826856136322},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.7107588648796082},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5429672002792358},{"id":"https://openalex.org/keywords/exponential-family","display_name":"Exponential family","score":0.5152507424354553},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49152740836143494},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4623357951641083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45473864674568176},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4435860514640808},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4374033808708191},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.43432095646858215},{"id":"https://openalex.org/keywords/information-criteria","display_name":"Information Criteria","score":0.4278413951396942},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4102294445037842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37702760100364685}],"concepts":[{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.8447777032852173},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.716826856136322},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.7107588648796082},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5429672002792358},{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.5152507424354553},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49152740836143494},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4623357951641083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45473864674568176},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4435860514640808},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4374033808708191},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.43432095646858215},{"id":"https://openalex.org/C2776709221","wikidata":"https://www.wikidata.org/wiki/Q6031040","display_name":"Information Criteria","level":3,"score":0.4278413951396942},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4102294445037842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37702760100364685},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/lsp.2012.2206583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2012.2206583","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Letters","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":17,"referenced_works":["https://openalex.org/W1486632395","https://openalex.org/W1488105383","https://openalex.org/W1501627432","https://openalex.org/W1546503963","https://openalex.org/W1814500939","https://openalex.org/W2008414513","https://openalex.org/W2010000350","https://openalex.org/W2019841176","https://openalex.org/W2054658115","https://openalex.org/W2084089095","https://openalex.org/W2102098892","https://openalex.org/W2142635246","https://openalex.org/W2144106004","https://openalex.org/W2156755023","https://openalex.org/W2168175751","https://openalex.org/W4241962560","https://openalex.org/W6628849183"],"related_works":["https://openalex.org/W2114625140","https://openalex.org/W1968886786","https://openalex.org/W2607006239","https://openalex.org/W304661108","https://openalex.org/W2509820422","https://openalex.org/W3122388856","https://openalex.org/W1502087262","https://openalex.org/W2963415778","https://openalex.org/W2033907215","https://openalex.org/W236172754"],"abstract_inverted_index":{"Model":[0],"selection":[1,41,88],"is":[2],"an":[3,29],"important":[4],"task":[5],"in":[6,28,34,94],"many":[7],"signal":[8],"processing":[9],"applications.":[10],"In":[11],"this":[12],"letter,":[13],"we":[14,81,104],"present":[15],"a":[16,95],"generalized":[17],"likelihood":[18],"ratio":[19],"(GLR)-based":[20],"derivation":[21],"of":[22,38,65,89,92,102],"the":[23,35,63,70,77,87,90],"recently":[24],"proposed":[25],"EEF":[26,33,55,107],"rule":[27],"attempt":[30],"to":[31,59,69,110],"cast":[32],"main":[36],"stream":[37],"model":[39,98],"order":[40],"approaches":[42],"and":[43],"provide":[44],"further":[45],"insights":[46],"into":[47],"its":[48],"theoretical":[49],"foundations.":[50],"We":[51],"also":[52],"show":[53,105],"that":[54,106],"can":[56],"be":[57],"expected":[58],"behave":[60],"asymptotically":[61],"(in":[62],"number":[64,91],"data":[66],"samples)":[67],"similarly":[68,109],"Bayesian":[71],"information":[72],"criterion":[73],"(BIC).":[74],"To":[75],"evaluate":[76],"finite":[78],"sample":[79],"performance":[80],"consider":[82],"two":[83],"numerical":[84],"examples,":[85],"including":[86],"components":[93],"Gaussian":[96],"mixture":[97],"(GMM),":[99],"by":[100],"means":[101],"which":[103],"behaves":[108],"BIC.":[111]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}