{"id":"https://openalex.org/W4380052707","doi":"https://doi.org/10.1214/23-ba1392","title":"A Bayesian Sparse Factor Model with Adaptive Posterior Concentration","display_name":"A Bayesian Sparse Factor Model with Adaptive Posterior Concentration","publication_year":2023,"publication_date":"2023-06-08","ids":{"openalex":"https://openalex.org/W4380052707","doi":"https://doi.org/10.1214/23-ba1392"},"language":"en","primary_location":{"id":"doi:10.1214/23-ba1392","doi":"https://doi.org/10.1214/23-ba1392","is_oa":true,"landing_page_url":"https://doi.org/10.1214/23-ba1392","pdf_url":"https://projecteuclid.org/journals/bayesian-analysis/advance-publication/A-Bayesian-Sparse-Factor-Model-with-Adaptive-Posterior-Concentration/10.1214/23-BA1392.pdf","source":{"id":"https://openalex.org/S127898559","display_name":"Bayesian Analysis","issn_l":"1931-6690","issn":["1931-6690","1936-0975"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","doyens","jufo-2","norway-2","oarl","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310321640","host_organization_name":"International Society for Bayesian Analysis","host_organization_lineage":["https://openalex.org/P4310321640"],"host_organization_lineage_names":["International Society for Bayesian Analysis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Analysis","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://projecteuclid.org/journals/bayesian-analysis/advance-publication/A-Bayesian-Sparse-Factor-Model-with-Adaptive-Posterior-Concentration/10.1214/23-BA1392.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022240133","display_name":"Ilsang Ohn","orcid":"https://orcid.org/0000-0003-3953-5953","observed_orcids":["https://orcid.org/0000-0003-3953-5953"]},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ilsang Ohn","raw_affiliation_strings":["Department of Statistics, Inha University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Inha University","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066307947","display_name":"Lizhen Lin","orcid":"https://orcid.org/0000-0002-7913-2780","observed_orcids":["https://orcid.org/0000-0002-7913-2780"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lizhen Lin","raw_affiliation_strings":["Department of Applied and Computational Mathematics and Statistics, The University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied and Computational Mathematics and Statistics, The University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011264910","display_name":"Yongdai Kim","orcid":"https://orcid.org/0000-0002-9434-5645","observed_orcids":["https://orcid.org/0000-0002-9434-5645"]},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yongdai Kim","raw_affiliation_strings":["Department of Statistics, Seoul National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.613,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66201818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"19","issue":"4","first_page":"1277","last_page":"1301"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9916999936103821,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9916999936103821,"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":0.0017000000225380063,"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/T11716","display_name":"Random Matrices and Applications","score":0.0007999999797903001,"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/sparse-factor-models","display_name":"sparse factor models","score":0.921999990940094},{"id":"https://openalex.org/keywords/high-dimensional-factor-models","display_name":"high-dimensional factor models","score":0.7480000257492065},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6510000228881836},{"id":"https://openalex.org/keywords/posterior-concentration","display_name":"posterior concentration","score":0.6970000267028809},{"id":"https://openalex.org/keywords/posterior-consistency","display_name":"posterior consistency","score":0.9449999928474426},{"id":"https://openalex.org/keywords/detection-rate","display_name":"detection rate","score":0.7440000176429749},{"id":"https://openalex.org/keywords/covariance-matrix-estimation","display_name":"covariance matrix estimation","score":0.8889999985694885}],"concepts":[{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.7521665692329407},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5831046104431152},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5530786514282227},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5196117758750916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5138233304023743},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.46254488825798035},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45394471287727356},{"id":"https://openalex.org/C10879293","wikidata":"https://www.wikidata.org/wiki/Q726474","display_name":"Factor analysis","level":2,"score":0.4394167959690094},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.43374770879745483},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.42462658882141113},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.421189546585083},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33812132477760315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27061688899993896},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2253977656364441},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1214/23-ba1392","doi":"https://doi.org/10.1214/23-ba1392","is_oa":true,"landing_page_url":"https://doi.org/10.1214/23-ba1392","pdf_url":"https://projecteuclid.org/journals/bayesian-analysis/advance-publication/A-Bayesian-Sparse-Factor-Model-with-Adaptive-Posterior-Concentration/10.1214/23-BA1392.pdf","source":{"id":"https://openalex.org/S127898559","display_name":"Bayesian Analysis","issn_l":"1931-6690","issn":["1931-6690","1936-0975"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","doyens","jufo-2","norway-2","oarl","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310321640","host_organization_name":"International Society for Bayesian Analysis","host_organization_lineage":["https://openalex.org/P4310321640"],"host_organization_lineage_names":["International Society for Bayesian Analysis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Analysis","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2305.18488","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.18488","pdf_url":"https://arxiv.org/pdf/2305.18488","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2305.18488","doi":"https://doi.org/10.48550/arxiv.2305.18488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.18488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1214/23-ba1392","doi":"https://doi.org/10.1214/23-ba1392","is_oa":true,"landing_page_url":"https://doi.org/10.1214/23-ba1392","pdf_url":"https://projecteuclid.org/journals/bayesian-analysis/advance-publication/A-Bayesian-Sparse-Factor-Model-with-Adaptive-Posterior-Concentration/10.1214/23-BA1392.pdf","source":{"id":"https://openalex.org/S127898559","display_name":"Bayesian Analysis","issn_l":"1931-6690","issn":["1931-6690","1936-0975"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-b","doyens","jufo-2","norway-2","oarl","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310321640","host_organization_name":"International Society for Bayesian Analysis","host_organization_lineage":["https://openalex.org/P4310321640"],"host_organization_lineage_names":["International Society for Bayesian Analysis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bayesian Analysis","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2838263400","display_name":null,"funder_award_id":"2020R1A2C3A01003550","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G3983780851","display_name":"CAREER: Utilizing Geometry for Statistical Learning and Inference","funder_award_id":"1654579","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4610018812","display_name":null,"funder_award_id":"2020R1A2C3A01003550","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7390105355","display_name":"Deep Learning on Manifolds: New Architectures and Theoretical Foundations","funder_award_id":"2113642","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4380052707.pdf","grobid_xml":"https://content.openalex.org/works/W4380052707.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1589435094","https://openalex.org/W1593060747","https://openalex.org/W1593748280","https://openalex.org/W1625086225","https://openalex.org/W1802592362","https://openalex.org/W1970562500","https://openalex.org/W1975395389","https://openalex.org/W2002605354","https://openalex.org/W2019935777","https://openalex.org/W2024103316","https://openalex.org/W2029721016","https://openalex.org/W2030290173","https://openalex.org/W2031213242","https://openalex.org/W2035076036","https://openalex.org/W2040373108","https://openalex.org/W2043035462","https://openalex.org/W2051623559","https://openalex.org/W2053609837","https://openalex.org/W2066358593","https://openalex.org/W2069049877","https://openalex.org/W2073681337","https://openalex.org/W2079563517","https://openalex.org/W2112659545","https://openalex.org/W2124161652","https://openalex.org/W2136269382","https://openalex.org/W2136321181","https://openalex.org/W2147140611","https://openalex.org/W2157400927","https://openalex.org/W2160123652","https://openalex.org/W2225934353","https://openalex.org/W2514253892","https://openalex.org/W2963426032","https://openalex.org/W2964287564","https://openalex.org/W3015764491","https://openalex.org/W3031700746","https://openalex.org/W3088001967","https://openalex.org/W3098826229","https://openalex.org/W3100838142","https://openalex.org/W3103757612","https://openalex.org/W3121936541","https://openalex.org/W3154792754","https://openalex.org/W4205508580","https://openalex.org/W4247571494","https://openalex.org/W4287364344"],"related_works":["https://openalex.org/W3154792754","https://openalex.org/W3103757612","https://openalex.org/W2797300439","https://openalex.org/W101741844","https://openalex.org/W7213630809","https://openalex.org/W4317461631","https://openalex.org/W7165396747","https://openalex.org/W7095309345","https://openalex.org/W787109133","https://openalex.org/W2029721016"],"abstract_inverted_index":{"In":[0],"this":[1,75],"paper,":[2],"we":[3,122],"propose":[4],"a":[5,11,38,111,124],"new":[6],"Bayesian":[7,99],"inference":[8],"method":[9,144],"for":[10],"high-dimensional":[12],"sparse":[13,24],"factor":[14,20,47,70,108],"model":[15,100],"that":[16,61,96],"allows":[17],"both":[18],"the":[19,23,27,42,46,62,68,81,85,90,97,102,107,119,130,139,142],"dimensionality":[21,109],"and":[22,45,72,89,137],"structure":[25],"of":[26,84,106,129,141],"loading":[28,87],"matrix":[29,88],"to":[30,36,51,80],"be":[31],"inferred.":[32],"The":[33],"novelty":[34],"is":[35,78],"introduce":[37],"certain":[39],"dependence":[40],"between":[41],"sparsity":[43,82],"level":[44,83],"dimensionality,":[48,71],"which":[49],"leads":[50],"adaptive":[52,79],"posterior":[53,63,76,126],"concentration":[54,127],"while":[55],"keeping":[56],"computational":[57],"tractability.":[58],"We":[59,93],"show":[60,138],"distribution":[64],"asymptotically":[65],"concentrates":[66],"on":[67],"true":[69,86],"more":[73,112],"importantly,":[74],"consistency":[77],"noise":[91],"variance.":[92],"also":[94],"prove":[95],"proposed":[98,143],"attains":[101],"optimal":[103],"detection":[104],"rate":[105,128],"in":[110,118],"general":[113],"situation":[114],"than":[115],"those":[116],"found":[117],"literature.":[120],"Moreover,":[121],"obtain":[123],"near-optimal":[125],"covariance":[131],"matrix.":[132],"Numerical":[133],"studies":[134],"are":[135],"conducted":[136],"superiority":[140],"compared":[145],"with":[146],"other":[147],"competitors.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-06-10T00:00:00"}
