{"id":"https://openalex.org/W3120768146","doi":"https://doi.org/10.1101/2021.01.13.21249759","title":"Bayesian Variable Selection Utilizing Posterior Probability Credible Intervals","display_name":"Bayesian Variable Selection Utilizing Posterior Probability Credible Intervals","publication_year":2021,"publication_date":"2021-01-15","ids":{"openalex":"https://openalex.org/W3120768146","doi":"https://doi.org/10.1101/2021.01.13.21249759","mag":"3120768146"},"language":"en","primary_location":{"id":"doi:10.1101/2021.01.13.21249759","doi":"https://doi.org/10.1101/2021.01.13.21249759","is_oa":true,"landing_page_url":"https://doi.org/10.1101/2021.01.13.21249759","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2021/01/15/2021.01.13.21249759.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.medrxiv.org/content/medrxiv/early/2021/01/15/2021.01.13.21249759.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053417868","display_name":"Mengtian Du","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mengtian Du","raw_affiliation_strings":["Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118 E-mail:","Department of Biostatistics, Boston University School of Public Health, 80 Massachusetts Avenue 3rd Floor, Boston, MA 028 E-mail:"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118 E-mail:","institution_ids":["https://openalex.org/I111088046"]},{"raw_affiliation_string":"Department of Biostatistics, Boston University School of Public Health, 80 Massachusetts Avenue 3rd Floor, Boston, MA 028 E-mail:","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102850471","display_name":"Stacy L. Andersen","orcid":"https://orcid.org/0000-0002-2442-6386","observed_orcids":["https://orcid.org/0000-0002-2442-6386"]},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Stacy L. Andersen","raw_affiliation_strings":["Geriatrics Section, Department of Medicine, Boston University School of Medicine, 72 E. Concord St Street L912, Boston, MA 02118 E-mail:  E-mail:","Geriatrics Section, Department of Medicine, Boston University School of Medicine, 7 E. Concord St Street L91, Boston, MA 0118 E-mail: E-mail:"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geriatrics Section, Department of Medicine, Boston University School of Medicine, 72 E. Concord St Street L912, Boston, MA 02118 E-mail:  E-mail:","institution_ids":["https://openalex.org/I111088046"]},{"raw_affiliation_string":"Geriatrics Section, Department of Medicine, Boston University School of Medicine, 7 E. Concord St Street L91, Boston, MA 0118 E-mail: E-mail:","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031579243","display_name":"Thomas T. Perls","orcid":"https://orcid.org/0000-0002-2492-4334","observed_orcids":["https://orcid.org/0000-0002-2492-4334","https://orcid.org/0000-0003-1545-1720"]},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thomas T. Perls","raw_affiliation_strings":["Geriatrics Section, Department of Medicine, Boston University School of Medicine, 72 E. Concord St Street L912, Boston, MA 02118 E-mail:  E-mail:","Geriatrics Section, Department of Medicine, Boston University School of Medicine, 7 E. Concord St Street L91, Boston, MA 0118 E-mail: E-mail:"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geriatrics Section, Department of Medicine, Boston University School of Medicine, 72 E. Concord St Street L912, Boston, MA 02118 E-mail:  E-mail:","institution_ids":["https://openalex.org/I111088046"]},{"raw_affiliation_string":"Geriatrics Section, Department of Medicine, Boston University School of Medicine, 7 E. Concord St Street L91, Boston, MA 0118 E-mail: E-mail:","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003627453","display_name":"Paola Sebastiani B","orcid":"https://orcid.org/0000-0001-6419-1545","observed_orcids":["https://orcid.org/0000-0001-6419-1545"]},"institutions":[{"id":"https://openalex.org/I1290900354","display_name":"Tufts Medical Center","ror":"https://ror.org/002hsbm82","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1290900354"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Paola Sebastiani","raw_affiliation_strings":["Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Boston, MA 02111 E-mail:"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Boston, MA 02111 E-mail:","institution_ids":["https://openalex.org/I1290900354"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003627453","https://openalex.org/A5031579243","https://openalex.org/A5053417868","https://openalex.org/A5102850471"],"corresponding_institution_ids":["https://openalex.org/I111088046","https://openalex.org/I1290900354"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.98580002784729,"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":0.98580002784729,"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":0.006500000134110451,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.0038999998942017555,"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/bayesian-variable-selection","display_name":"Bayesian variable selection","score":0.9810000061988831},{"id":"https://openalex.org/keywords/credible-intervals","display_name":"credible intervals","score":0.7360000014305115},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"posterior probability","score":0.6980000138282776},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.9570000171661377},{"id":"https://openalex.org/keywords/deviance-information-criterion","display_name":"deviance information criterion","score":0.8849999904632568},{"id":"https://openalex.org/keywords/stochastic-search-variable-selection","display_name":"stochastic search variable selection","score":0.9070000052452087},{"id":"https://openalex.org/keywords/bayesian-lasso","display_name":"Bayesian LASSO","score":0.996999979019165},{"id":"https://openalex.org/keywords/prior-sensitivity-analysis","display_name":"prior sensitivity","score":0.7059999704360962},{"id":"https://openalex.org/keywords/mixed-effects-models","display_name":"mixed models","score":0.7459999918937683},{"id":"https://openalex.org/keywords/model-parsimony","display_name":"model parsimony","score":0.8799999952316284}],"concepts":[{"id":"https://openalex.org/C17634605","wikidata":"https://www.wikidata.org/wiki/Q972431","display_name":"Deviance information criterion","level":4,"score":0.7929749488830566},{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.7142019271850586},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6355429887771606},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6283174157142639},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.6229175329208374},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6183927059173584},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.6144712567329407},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5988641977310181},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4909954369068146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47936445474624634},{"id":"https://openalex.org/C2776709221","wikidata":"https://www.wikidata.org/wiki/Q6031040","display_name":"Information Criteria","level":3,"score":0.4384404718875885},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.428221732378006},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39403313398361206},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38566112518310547},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3589684069156647},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3200390338897705}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1101/2021.01.13.21249759","doi":"https://doi.org/10.1101/2021.01.13.21249759","is_oa":true,"landing_page_url":"https://doi.org/10.1101/2021.01.13.21249759","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2021/01/15/2021.01.13.21249759.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.1101/2021.01.13.21249759","doi":"https://doi.org/10.1101/2021.01.13.21249759","is_oa":true,"landing_page_url":"https://doi.org/10.1101/2021.01.13.21249759","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2021/01/15/2021.01.13.21249759.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3120768146.pdf","grobid_xml":"https://content.openalex.org/works/W3120768146.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W140571646","https://openalex.org/W171292237","https://openalex.org/W1603353793","https://openalex.org/W1982652137","https://openalex.org/W1985497040","https://openalex.org/W1999974018","https://openalex.org/W2007069447","https://openalex.org/W2026834839","https://openalex.org/W2047554048","https://openalex.org/W2049228615","https://openalex.org/W2057765075","https://openalex.org/W2078120857","https://openalex.org/W2085890860","https://openalex.org/W2099800366","https://openalex.org/W2106706098","https://openalex.org/W2120216197","https://openalex.org/W2147848557","https://openalex.org/W2159529587","https://openalex.org/W2612470344","https://openalex.org/W2884284039","https://openalex.org/W2904925972","https://openalex.org/W3103509914","https://openalex.org/W3159844608"],"related_works":["https://openalex.org/W49963470","https://openalex.org/W7075761327","https://openalex.org/W1484976773","https://openalex.org/W7096982312","https://openalex.org/W2353230564","https://openalex.org/W7097627166","https://openalex.org/W2275598437","https://openalex.org/W2013901661","https://openalex.org/W7097488878","https://openalex.org/W2049228615"],"abstract_inverted_index":{"Abstract":[0],"In":[1],"recent":[2],"years,":[3],"there":[4],"has":[5,132],"been":[6],"growing":[7],"interest":[8],"in":[9,15,53,171,176],"the":[10,16,47,54,58,80,88,97,103,119,133,160,166,172],"problem":[11],"of":[12,61,79,100,118,129,141],"model":[13,26,42,64],"selection":[14,140,156],"Bayesian":[17,36,116],"framework.":[18],"Current":[19],"approaches":[20],"include":[21],"methods":[22,39,93],"based":[23,40,82],"on":[24,41,83,87,96,187],"computing":[25],"probabilities":[27,60],"such":[28,45],"as":[29,46],"Stochastic":[30],"Search":[31],"Variable":[32],"Selection":[33],"(SSVS)":[34],"and":[35,38,75,102,180,193],"LASSO":[37],"choice":[43],"criteria,":[44],"Deviance":[48],"Information":[49,121],"Criterion":[50,122],"(DIC).":[51],"Methods":[52],"first":[55],"group":[56],"compute":[57],"posterior":[59,89],"models":[62,144],"or":[63],"parameters":[65,101,161],"often":[66],"using":[67],"a":[68,77,84,115,153,177,181],"Markov":[69],"Chain":[70],"Monte":[71],"Carlo":[72],"(MCMC)":[73],"technique,":[74],"select":[76,165],"subset":[78],"variables":[81,167],"prespecified":[85],"threshold":[86],"probability.":[90],"However,":[91],"these":[92],"rely":[94],"heavily":[95],"prior":[98],"choices":[99],"results":[104],"can":[105,136],"be":[106,137,169],"highly":[107],"sensitive":[108],"when":[109],"priors":[110],"are":[111],"changed.":[112],"DIC":[113,192],"is":[114],"generalization":[117],"Akaike\u2019s":[120],"(AIC)":[123],"that":[124,135,158,184],"penalizes":[125],"for":[126,139],"large":[127],"number":[128],"parameters,":[130],"it":[131],"advantage":[134],"used":[138],"mixed":[142],"effect":[143],"but":[145],"tends":[146],"to":[147,164,168],"prefer":[148],"overparameterized":[149],"models.":[150,197],"We":[151,174],"propose":[152],"novel":[154],"variable":[155],"algorithm":[157,186],"utilizes":[159],"credible":[162],"intervals":[163],"kept":[170],"model.":[173],"show":[175],"simulation":[178],"study":[179],"real-world":[182],"example":[183],"this":[185],"average":[188],"performs":[189],"better":[190],"than":[191],"produces":[194],"more":[195],"parsimonious":[196]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-10-17T09:40:24.721904","created_date":"2021-01-18T00:00:00"}