{"id":"https://openalex.org/W4386542199","doi":"https://doi.org/10.3390/e25091310","title":"Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models","display_name":"Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models","publication_year":2023,"publication_date":"2023-09-08","ids":{"openalex":"https://openalex.org/W4386542199","doi":"https://doi.org/10.3390/e25091310","pmid":"https://pubmed.ncbi.nlm.nih.gov/37761609"},"language":"en","primary_location":{"id":"doi:10.3390/e25091310","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25091310","pdf_url":"https://www.mdpi.com/1099-4300/25/9/1310/pdf?version=1694156117","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/25/9/1310/pdf?version=1694156117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074396461","display_name":"Xitong Liang","orcid":"https://orcid.org/0000-0002-2434-1841"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Xitong Liang","raw_affiliation_strings":["Department of Statistical Science, University College London, London WC1E 6BT, UK"],"raw_orcid":"https://orcid.org/0000-0002-2434-1841","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London WC1E 6BT, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077007118","display_name":"Samuel Livingstone","orcid":"https://orcid.org/0000-0002-7277-086X"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Samuel Livingstone","raw_affiliation_strings":["Department of Statistical Science, University College London, London WC1E 6BT, UK"],"raw_orcid":"https://orcid.org/0000-0002-7277-086X","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London WC1E 6BT, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015629228","display_name":"Jim E. Griffin","orcid":"https://orcid.org/0000-0002-4828-7368"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jim Griffin","raw_affiliation_strings":["Department of Statistical Science, University College London, London WC1E 6BT, UK"],"raw_orcid":"https://orcid.org/0000-0002-4828-7368","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London WC1E 6BT, UK","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074396461"],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":1.8944,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87160097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"25","issue":"9","first_page":"1310","last_page":"1310"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9993000030517578,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9975000023841858,"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/marginal-likelihood","display_name":"Marginal likelihood","score":0.6565088033676147},{"id":"https://openalex.org/keywords/laplaces-method","display_name":"Laplace's method","score":0.575283944606781},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5685636401176453},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5679347515106201},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5263468027114868},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.47925877571105957},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45647555589675903},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.4472995102405548},{"id":"https://openalex.org/keywords/reversible-jump-markov-chain-monte-carlo","display_name":"Reversible-jump Markov chain Monte Carlo","score":0.4395748972892761},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.43495625257492065},{"id":"https://openalex.org/keywords/marginal-model","display_name":"Marginal model","score":0.432911217212677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40910759568214417},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.39291495084762573},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23026800155639648},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.19313594698905945}],"concepts":[{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.6565088033676147},{"id":"https://openalex.org/C22243797","wikidata":"https://www.wikidata.org/wiki/Q2058297","display_name":"Laplace's method","level":3,"score":0.575283944606781},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5685636401176453},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5679347515106201},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5263468027114868},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.47925877571105957},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45647555589675903},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.4472995102405548},{"id":"https://openalex.org/C2780591659","wikidata":"https://www.wikidata.org/wiki/Q17083869","display_name":"Reversible-jump Markov chain Monte Carlo","level":4,"score":0.4395748972892761},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.43495625257492065},{"id":"https://openalex.org/C197656967","wikidata":"https://www.wikidata.org/wiki/Q17058458","display_name":"Marginal model","level":3,"score":0.432911217212677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40910759568214417},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39291495084762573},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23026800155639648},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.19313594698905945}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e25091310","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25091310","pdf_url":"https://www.mdpi.com/1099-4300/25/9/1310/pdf?version=1694156117","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:37761609","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37761609","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10528396","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10528396","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10528396/pdf/entropy-25-01310.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Entropy (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:30b27be9635940b689898d99495cc847","is_oa":true,"landing_page_url":"https://doaj.org/article/30b27be9635940b689898d99495cc847","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 25, Iss 9, p 1310 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/25/9/1310/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e25091310","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Entropy","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e25091310","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25091310","pdf_url":"https://www.mdpi.com/1099-4300/25/9/1310/pdf?version=1694156117","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4624664370","display_name":null,"funder_award_id":"EP/V055380/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320320285","display_name":"King's College London","ror":"https://ror.org/0220mzb33"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386542199.pdf","grobid_xml":"https://content.openalex.org/works/W4386542199.grobid-xml"},"referenced_works_count":81,"referenced_works":["https://openalex.org/W1486016120","https://openalex.org/W1492534149","https://openalex.org/W1523985187","https://openalex.org/W1543012556","https://openalex.org/W1666386643","https://openalex.org/W1830883013","https://openalex.org/W1860392020","https://openalex.org/W1928145421","https://openalex.org/W1989757660","https://openalex.org/W1999974018","https://openalex.org/W2004141349","https://openalex.org/W2006273711","https://openalex.org/W2008966356","https://openalex.org/W2011471859","https://openalex.org/W2012551717","https://openalex.org/W2020389170","https://openalex.org/W2031452361","https://openalex.org/W2040042308","https://openalex.org/W2056760934","https://openalex.org/W2057765075","https://openalex.org/W2091860746","https://openalex.org/W2099878672","https://openalex.org/W2100165425","https://openalex.org/W2104840646","https://openalex.org/W2114169935","https://openalex.org/W2123222757","https://openalex.org/W2135973421","https://openalex.org/W2138309709","https://openalex.org/W2144898279","https://openalex.org/W2149441684","https://openalex.org/W2150847344","https://openalex.org/W2152977846","https://openalex.org/W2153151760","https://openalex.org/W2154187696","https://openalex.org/W2159325249","https://openalex.org/W2168175751","https://openalex.org/W2225156818","https://openalex.org/W2240102487","https://openalex.org/W2341128841","https://openalex.org/W2375577403","https://openalex.org/W2400086120","https://openalex.org/W2474656875","https://openalex.org/W2514584907","https://openalex.org/W2542256003","https://openalex.org/W2602449753","https://openalex.org/W2788414627","https://openalex.org/W2793956875","https://openalex.org/W2798324097","https://openalex.org/W2904648838","https://openalex.org/W2938262174","https://openalex.org/W2951673559","https://openalex.org/W2955592940","https://openalex.org/W2962690136","https://openalex.org/W2962997929","https://openalex.org/W2963254535","https://openalex.org/W2963937909","https://openalex.org/W2966361766","https://openalex.org/W3092343786","https://openalex.org/W3093548819","https://openalex.org/W3093660365","https://openalex.org/W3100622648","https://openalex.org/W3102025474","https://openalex.org/W3102137642","https://openalex.org/W3105257655","https://openalex.org/W3113346884","https://openalex.org/W3118851742","https://openalex.org/W3125452207","https://openalex.org/W3127306259","https://openalex.org/W3199916448","https://openalex.org/W3208535748","https://openalex.org/W4200319157","https://openalex.org/W4234467434","https://openalex.org/W4245958676","https://openalex.org/W4252732282","https://openalex.org/W4283514718","https://openalex.org/W4307734616","https://openalex.org/W4310153751","https://openalex.org/W6677465874","https://openalex.org/W6678521640","https://openalex.org/W6712833629","https://openalex.org/W6784386773"],"related_works":["https://openalex.org/W2176075850","https://openalex.org/W3125631512","https://openalex.org/W2132339668","https://openalex.org/W2093345115","https://openalex.org/W2550112186","https://openalex.org/W2009591042","https://openalex.org/W1976121415","https://openalex.org/W2142258617","https://openalex.org/W3124166636","https://openalex.org/W4386542199"],"abstract_inverted_index":{"Developing":[0],"an":[1,141,181],"efficient":[2,182],"computational":[3],"scheme":[4,84],"for":[5,89,104,187],"high-dimensional":[6,235],"Bayesian":[7],"variable":[8],"selection":[9],"in":[10,170],"generalised":[11,106,163],"linear":[12,107,164],"models":[13,16,49,155,165],"and":[14,50,109,112,166,183,221,233],"survival":[15,110,167],"has":[17],"always":[18],"been":[19],"a":[20,82,118,122,197,218],"challenging":[21],"problem":[22],"due":[23],"to":[24,30,46,68,152,200,239],"the":[25,31,53,57,71,114,158,173,202,207,215,222,241,244,250],"absence":[26],"of":[27,56,60,145,162,172,206,217,243],"closed-form":[28],"solutions":[29],"marginal":[32,72,115,159,188],"likelihood.":[33],"The":[34],"Reversible":[35],"Jump":[36],"Markov":[37],"Chain":[38],"Monte":[39],"Carlo":[40],"(RJMCMC)":[41],"approach":[42],"can":[43,62,74,126],"be":[44,63,75,127],"employed":[45],"jointly":[47],"sample":[48,154],"coefficients,":[51],"but":[52],"effective":[54],"design":[55],"trans-dimensional":[58],"jumps":[59],"RJMCMC":[61],"challenging,":[64],"making":[65],"it":[66],"hard":[67],"implement.":[69],"Alternatively,":[70],"likelihood":[73,116,189],"derived":[76],"conditional":[77],"on":[78],"latent":[79],"variables":[80],"using":[81,93,117],"data-augmentation":[83,99],"(e.g.,":[85],"P\u00f3lya-gamma":[86],"data":[87,232],"augmentation":[88],"logistic":[90],"regression)":[91],"or":[92,121],"other":[94],"estimation":[95,185],"methods.":[96],"However,":[97],"suitable":[98],"schemes":[100],"are":[101,136],"not":[102],"available":[103],"every":[105],"model":[108],"model,":[111],"estimating":[113],"Laplace":[119,177],"approximation":[120],"correlated":[123],"pseudo-marginal":[124],"method":[125,186,199],"computationally":[128],"expensive.":[129],"In":[130],"this":[131],"paper,":[132],"three":[133],"main":[134],"contributions":[135],"presented.":[137],"Firstly,":[138],"we":[139,179,195],"present":[140,226],"extended":[142],"Point-wise":[143],"implementation":[144],"Adaptive":[146],"Random":[147],"Neighbourhood":[148],"Informed":[149],"proposal":[150,209,247],"(PARNI)":[151],"efficiently":[153],"directly":[156],"from":[157,230],"posterior":[160],"distributions":[161],"models.":[168],"Secondly,":[169],"light":[171],"recently":[174],"proposed":[175],"approximate":[176],"approximation,":[178],"describe":[180,196],"accurate":[184],"that":[190],"involves":[191],"adaptive":[192],"parameters.":[193],"Additionally,":[194],"new":[198],"adapt":[201],"algorithmic":[203],"tuning":[204],"parameters":[205],"PARNI":[208,246],"by":[210],"replacing":[211],"Rao-Blackwellised":[212],"estimates":[213],"with":[214,249],"combination":[216],"warm-start":[219],"estimate":[220],"ergodic":[223],"average.":[224],"We":[225],"numerous":[227],"numerical":[228],"results":[229],"simulated":[231],"eight":[234],"genetic":[236],"mapping":[237],"data-sets":[238],"showcase":[240],"efficiency":[242],"novel":[245],"compared":[248],"baseline":[251],"add\u2013delete\u2013swap":[252],"proposal.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}