{"id":"https://openalex.org/W7146974274","doi":"https://doi.org/10.48550/arxiv.2603.27142","title":"tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data","display_name":"tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data","publication_year":2026,"publication_date":"2026-03-28","ids":{"openalex":"https://openalex.org/W7146974274","doi":"https://doi.org/10.48550/arxiv.2603.27142"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27142","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.27142","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132591099","display_name":"Amuche Ibenegbu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ibenegbu, Amuche","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021848861","display_name":"Pierre Lafaye de Micheaux","orcid":"https://orcid.org/0000-0002-0247-5136"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Micheaux, Pierre Lafaye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5104090323","display_name":"Rohitash Chandra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chandra, Rohitash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.2676999866962433,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.2676999866962433,"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/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.17409999668598175,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.048900000751018524,"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/imputation","display_name":"Imputation (statistics)","score":0.8458999991416931},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7434999942779541},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6157000064849854},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.569100022315979},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.47920000553131104},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.45890000462532043},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.41510000824928284},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.36149999499320984},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.36059999465942383}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8458999991416931},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7434999942779541},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6157000064849854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5921000242233276},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.569100022315979},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.45890000462532043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44290000200271606},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.41510000824928284},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3993000090122223},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.3603000044822693},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33739998936653137},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.29899999499320984},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.27810001373291016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2777000069618225},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.2655999958515167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2628999948501587},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27142","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.27142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27142","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.5745470523834229}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"analysis":[1],"is":[2],"often":[3],"affected":[4],"by":[5,21],"missing":[6,30,49],"data,":[7],"a":[8,142,179],"common":[9],"problem":[10],"across":[11,158],"several":[12],"fields,":[13],"including":[14],"healthcare":[15],"and":[16,66,75,99,101,109,132,181,198],"environmental":[17,197],"monitoring.":[18],"Multiple":[19],"Imputation":[20],"Chained":[22],"Equations":[23],"(MICE)":[24],"has":[25],"been":[26],"prominent":[27],"for":[28,60,134],"imputing":[29],"values":[31,50],"through":[32],"\"fully":[33],"conditional":[34],"specification\".":[35],"We":[36,69,89,149],"extend":[37],"MICE":[38,63],"using":[39,94,102],"the":[40,79,83,91,104,110,126,137,175],"Bayesian":[41,45],"framework":[42,177],"(tBayes-MICE),":[43],"utilising":[44],"inference":[46],"to":[47,58,81,125,184],"impute":[48],"via":[51],"Markov":[52],"Chain":[53],"Monte":[54],"Carlo":[55],"(MCMC)":[56],"sampling":[57],"account":[59],"uncertainty":[61,135,194],"in":[62,78,136,195],"model":[64,80],"parameters":[65],"imputed":[67],"values.":[68],"also":[70,150],"include":[71],"temporally":[72],"informed":[73],"initialisation":[74],"time-lagged":[76],"features":[77],"respect":[82],"sequential":[84],"nature":[85],"of":[86,146,193],"time-series":[87,185],"data.":[88],"evaluate":[90],"tBayes-MICE":[92,120,176],"method":[93],"two":[95],"real-world":[96],"datasets":[97],"(AirQuality":[98],"PhysioNet),":[100],"both":[103],"Random":[105],"Walk":[106],"Metropolis":[107],"(RWM)":[108],"Metropolis-Adjusted":[111],"Langevin":[112],"Algorithm":[113],"(MALA)":[114],"samplers.":[115],"Our":[116],"results":[117],"demonstrate":[118],"that":[119,152,174],"reduces":[121],"imputation":[122,138,147],"errors":[123],"relative":[124],"baseline":[127],"methods":[128],"over":[129],"all":[130],"variables":[131],"accounts":[133],"process,":[139],"thereby":[140],"providing":[141,165],"more":[143,166],"accurate":[144],"measure":[145],"error.":[148],"found":[151],"MALA":[153],"mixed":[154],"better":[155],"than":[156],"RWM":[157],"most":[159],"variables,":[160],"achieving":[161],"comparable":[162],"accuracy":[163,189],"while":[164],"consistent":[167],"posterior":[168],"exploration.":[169],"Overall,":[170],"these":[171],"findings":[172],"suggest":[173],"represents":[178],"practical":[180],"efficient":[182],"approach":[183],"imputation,":[186],"balancing":[187],"increased":[188],"with":[190],"meaningful":[191],"quantification":[192],"various":[196],"clinical":[199],"settings.":[200]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-02T00:00:00"}
