{"id":"https://openalex.org/W4390697915","doi":"https://doi.org/10.1007/s11222-023-10358-5","title":"Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms","display_name":"Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms","publication_year":2024,"publication_date":"2024-01-10","ids":{"openalex":"https://openalex.org/W4390697915","doi":"https://doi.org/10.1007/s11222-023-10358-5"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-023-10358-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-023-10358-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10358-5.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10358-5.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001199768","display_name":"Luca Brusa","orcid":"https://orcid.org/0000-0002-8156-470X"},"institutions":[{"id":"https://openalex.org/I66752286","display_name":"University of Milano-Bicocca","ror":"https://ror.org/01ynf4891","country_code":"IT","type":"education","lineage":["https://openalex.org/I66752286"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Luca Brusa","raw_affiliation_strings":["Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126, Milan, Italy","Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi\u00a08, 20126, Milan, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126, Milan, Italy","institution_ids":["https://openalex.org/I66752286"]},{"raw_affiliation_string":"Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi\u00a08, 20126, Milan, Italy","institution_ids":["https://openalex.org/I66752286"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021943787","display_name":"Fulvia Pennoni","orcid":"https://orcid.org/0000-0002-6331-7211"},"institutions":[{"id":"https://openalex.org/I66752286","display_name":"University of Milano-Bicocca","ror":"https://ror.org/01ynf4891","country_code":"IT","type":"education","lineage":["https://openalex.org/I66752286"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fulvia Pennoni","raw_affiliation_strings":["Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126, Milan, Italy","Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi\u00a08, 20126, Milan, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126, Milan, Italy","institution_ids":["https://openalex.org/I66752286"]},{"raw_affiliation_string":"Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi\u00a08, 20126, Milan, Italy","institution_ids":["https://openalex.org/I66752286"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054448743","display_name":"Francesco Bartolucci","orcid":"https://orcid.org/0000-0001-7057-1421"},"institutions":[{"id":"https://openalex.org/I27483092","display_name":"University of Perugia","ror":"https://ror.org/00x27da85","country_code":"IT","type":"education","lineage":["https://openalex.org/I27483092"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Bartolucci","raw_affiliation_strings":["Department of Economics, University of Perugia, Via Alessandro Pascoli 20, 06123, Perugia, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Economics, University of Perugia, Via Alessandro Pascoli 20, 06123, Perugia, Italy","institution_ids":["https://openalex.org/I27483092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001199768"],"corresponding_institution_ids":["https://openalex.org/I66752286"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":2.3962,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89552288,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"34","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9993000030517578,"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":0.9993000030517578,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9925000071525574,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9850999712944031,"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/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.7189517617225647},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.6509969830513},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.6117706894874573},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5698701739311218},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5569449067115784},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.5350778102874756},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.46762093901634216},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.4579431414604187},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.4525550603866577},{"id":"https://openalex.org/keywords/stochastic-block-model","display_name":"Stochastic block model","score":0.4265739917755127},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.4197176396846771},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.405365526676178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38536879420280457},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3382929563522339},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.13238826394081116}],"concepts":[{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.7189517617225647},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.6509969830513},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.6117706894874573},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5698701739311218},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5569449067115784},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.5350778102874756},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.46762093901634216},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.4579431414604187},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4525550603866577},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.4265739917755127},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.4197176396846771},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.405365526676178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38536879420280457},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3382929563522339},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.13238826394081116}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s11222-023-10358-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-023-10358-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10358-5.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},{"id":"pmh:oai:boa.unimib.it:10281/455819","is_oa":true,"landing_page_url":"https://hdl.handle.net/10281/455819","pdf_url":"https://boa.unimib.it/bitstream/10281/455819/1/Brusa-2024-Stat%20Comput-VoR.pdf","source":{"id":"https://openalex.org/S4306401259","display_name":"BOA (University of Milano-Bicocca)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66752286","host_organization_name":"University of Milano-Bicocca","host_organization_lineage":["https://openalex.org/I66752286"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s11222-023-10358-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-023-10358-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-023-10358-5.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321610","display_name":"Universit\u00e0 degli Studi di Milano-Bicocca","ror":"https://ror.org/01ynf4891"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4390697915.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W129305155","https://openalex.org/W1497939657","https://openalex.org/W1516111018","https://openalex.org/W1559956479","https://openalex.org/W1568404366","https://openalex.org/W1595498733","https://openalex.org/W1965555277","https://openalex.org/W1991133427","https://openalex.org/W2046962184","https://openalex.org/W2049633694","https://openalex.org/W2062828949","https://openalex.org/W2086699924","https://openalex.org/W2088697515","https://openalex.org/W2102907934","https://openalex.org/W2109820980","https://openalex.org/W2114244473","https://openalex.org/W2115881827","https://openalex.org/W2116954712","https://openalex.org/W2120529703","https://openalex.org/W2124369546","https://openalex.org/W2125252492","https://openalex.org/W2133095386","https://openalex.org/W2133403695","https://openalex.org/W2144799688","https://openalex.org/W2168175751","https://openalex.org/W2171149164","https://openalex.org/W2257678991","https://openalex.org/W2468279777","https://openalex.org/W2488678869","https://openalex.org/W2554987453","https://openalex.org/W2766234915","https://openalex.org/W3048364984","https://openalex.org/W3101413764","https://openalex.org/W3124427131","https://openalex.org/W3132667749","https://openalex.org/W4230781079","https://openalex.org/W4256357823","https://openalex.org/W4303183149"],"related_works":["https://openalex.org/W4230230730","https://openalex.org/W4237379778","https://openalex.org/W1535265092","https://openalex.org/W3109783536","https://openalex.org/W2103023456","https://openalex.org/W1501016332","https://openalex.org/W2898901530","https://openalex.org/W1863135743","https://openalex.org/W2162880285","https://openalex.org/W2150921722"],"abstract_inverted_index":{"Abstract":[0],"We":[1,103],"propose":[2],"an":[3,74],"evolutionary":[4],"optimization":[5],"method":[6],"for":[7,127],"maximum":[8,12,114],"likelihood":[9,13],"and":[10,31,43,90,94,100,119,138,145],"approximate":[11],"estimation":[14],"of":[15,27,60,65,115,123,135],"discrete":[16],"latent":[17,86],"variable":[18],"models.":[19],"The":[20],"proposal":[21],"is":[22,71],"based":[23,38],"on":[24,39,132],"modified":[25],"versions":[26],"the":[28,40,49,53,61,66,97,109,116,124,133,147],"expectation\u2013maximization":[29],"(EM)":[30],"variational":[32],"EM":[33,99],"(VEM)":[34],"algorithms,":[35],"which":[36],"are":[37,82,141],"genetic":[41],"approach":[42],"allow":[44],"us":[45],"to":[46,55,84,111,143],"accurately":[47],"explore":[48],"parameter":[50],"space,":[51],"reducing":[52],"chance":[54,110],"be":[56],"trapped":[57],"into":[58],"one":[59],"multiple":[62],"local":[63],"maxima":[64],"log-likelihood":[67],"function.":[68],"Their":[69],"performance":[70],"examined":[72],"through":[73],"extensive":[75],"Monte":[76],"Carlo":[77],"simulation":[78],"study":[79],"where":[80],"they":[81],"employed":[83],"estimate":[85],"class,":[87],"hidden":[88],"Markov,":[89],"stochastic":[91],"block":[92],"models":[93],"compared":[95],"with":[96],"standard":[98],"VEM":[101],"algorithms.":[102,148],"observe":[104],"a":[105,120],"significant":[106],"increase":[107],"in":[108],"reach":[112],"global":[113],"target":[117],"function":[118],"high":[121],"accuracy":[122],"estimated":[125],"parameters":[126],"each":[128],"model.":[129],"Applications":[130],"focused":[131],"analysis":[134],"cross-sectional,":[136],"longitudinal,":[137],"network":[139],"data":[140],"proposed":[142],"illustrate":[144],"compare":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2024-01-13T00:00:00"}
