{"id":"https://openalex.org/W4281640962","doi":"https://doi.org/10.1080/03610918.2022.2076869","title":"Bayesian framework for interval-valued data using Jeffreys\u2019 prior and posterior predictive checking methods","display_name":"Bayesian framework for interval-valued data using Jeffreys\u2019 prior and posterior predictive checking methods","publication_year":2022,"publication_date":"2022-06-03","ids":{"openalex":"https://openalex.org/W4281640962","doi":"https://doi.org/10.1080/03610918.2022.2076869"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2022.2076869","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2076869","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100651041","display_name":"Min Xu","orcid":"https://orcid.org/0000-0002-3761-9048"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]},{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Xu","raw_affiliation_strings":["Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education","School of Economics and Management, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education","institution_ids":["https://openalex.org/I66867065"]},{"raw_affiliation_string":"School of Economics and Management, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009910997","display_name":"Zhongfeng Qin","orcid":"https://orcid.org/0000-0003-3875-2518"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhongfeng Qin","raw_affiliation_strings":["Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education","School of Economics and Management, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"School of Economics and Management, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009910997"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.05897731,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"53","issue":"5","first_page":"2425","last_page":"2443"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12135","display_name":"Fuzzy Systems and Optimization","score":0.9869999885559082,"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/T12135","display_name":"Fuzzy Systems and Optimization","score":0.9869999885559082,"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/T14351","display_name":"Statistical and Computational Modeling","score":0.9623000025749207,"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":0.9599999785423279,"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/posterior-probability","display_name":"Posterior probability","score":0.8419121503829956},{"id":"https://openalex.org/keywords/credible-interval","display_name":"Credible interval","score":0.6971607208251953},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6482791304588318},{"id":"https://openalex.org/keywords/posterior-predictive-distribution","display_name":"Posterior predictive distribution","score":0.6393401026725769},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5737400054931641},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5650238394737244},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.5398207902908325},{"id":"https://openalex.org/keywords/bayesian-hierarchical-modeling","display_name":"Bayesian hierarchical modeling","score":0.5099743604660034},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4913386404514313},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.4821602702140808},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.47810596227645874},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4730081856250763},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.43615245819091797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42433908581733704},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4183281362056732},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3604101538658142}],"concepts":[{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.8419121503829956},{"id":"https://openalex.org/C121117317","wikidata":"https://www.wikidata.org/wiki/Q4439221","display_name":"Credible interval","level":3,"score":0.6971607208251953},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6482791304588318},{"id":"https://openalex.org/C83247935","wikidata":"https://www.wikidata.org/wiki/Q7234227","display_name":"Posterior predictive distribution","level":5,"score":0.6393401026725769},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5737400054931641},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5650238394737244},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.5398207902908325},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.5099743604660034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4913386404514313},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.4821602702140808},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.47810596227645874},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4730081856250763},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.43615245819091797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42433908581733704},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4183281362056732},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3604101538658142},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2022.2076869","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2076869","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5699999928474426,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G4855141312","display_name":"\u4e0d\u786e\u5b9a\u73af\u5883\u4e0b\u5f15\u5165\u91d1\u878d\u5bf9\u51b2\u7684\u5e93\u5b58\u51b3\u7b56\u6a21\u578b\u53ca\u5176\u4f18\u5316\u7814\u7a76","funder_award_id":"71771011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7689300292","display_name":"\u57fa\u4e8e\u533a\u95f4\u6570\u636e\u7684\u8d1d\u53f6\u65af\u7ebf\u6027\u4e0e\u975e\u7ebf\u6027\u5efa\u6a21\u65b9\u6cd5\u53ca\u5176\u5e94\u7528\u7814\u7a76","funder_award_id":"72071008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1614215352","https://openalex.org/W1902943426","https://openalex.org/W1966139378","https://openalex.org/W1994440910","https://openalex.org/W2016006053","https://openalex.org/W2038318937","https://openalex.org/W2040388435","https://openalex.org/W2058251410","https://openalex.org/W2068708371","https://openalex.org/W2075498446","https://openalex.org/W2082269894","https://openalex.org/W2093709255","https://openalex.org/W2119981768","https://openalex.org/W2168934042","https://openalex.org/W2493977830","https://openalex.org/W2769240689","https://openalex.org/W2963039334","https://openalex.org/W2964218322","https://openalex.org/W4248681815"],"related_works":["https://openalex.org/W2966576817","https://openalex.org/W173785400","https://openalex.org/W4386758596","https://openalex.org/W2609999188","https://openalex.org/W4286429921","https://openalex.org/W48127374","https://openalex.org/W2032094637","https://openalex.org/W4382601126","https://openalex.org/W4241357250","https://openalex.org/W4235558569"],"abstract_inverted_index":{"Interval-valued":[0],"data":[1,61,111,126,139,153],"have":[2],"been":[3],"widely":[4],"used":[5],"in":[6],"both":[7],"academic":[8],"and":[9,51,75,94,150],"industry":[10],"research":[11],"areas.":[12],"With":[13],"the":[14,19,27,44,65,69,91,118,125,130,135],"development":[15],"of":[16,46,137,143],"computational":[17],"statistics,":[18],"Bayesian":[20,37,92],"methods":[21],"are":[22,78],"growing":[23],"rapidly.":[24],"Based":[25,85],"on":[26,86,113],"existing":[28],"theoretical":[29],"researches,":[30],"this":[31],"paper":[32],"tries":[33],"to":[34],"establish":[35],"a":[36,104,138],"framework":[38],"for":[39,59,64,98,109],"interval-valued":[40,60,99,110],"data,":[41],"which":[42],"contains":[43],"determination":[45],"Jeffreys\u2019":[47,57],"prior,":[48],"posterior":[49,52,70,76,87,105,119],"inferences,":[50],"predictive":[53,106,120],"checking":[54,107,121],"methods.":[55,84],"First,":[56],"prior":[58],"is":[62,73,146],"proposed":[63],"first":[66],"time.":[67],"Then,":[68],"conditional":[71],"distribution":[72,131],"derived,":[74],"samples":[77],"obtained":[79],"by":[80,148],"using":[81],"Gibbs":[82],"sampling":[83],"samples,":[88],"we":[89,102,133],"obtain":[90],"point":[93],"credible":[95],"interval":[96],"estimations":[97],"data.":[100],"Finally,":[101],"propose":[103],"method":[108,122,145],"based":[112],"novel":[114],"test":[115],"quantities.":[116],"If":[117],"shows":[123],"that":[124],"do":[127],"not":[128],"meet":[129],"assumptions,":[132],"suggest":[134],"use":[136],"transformation.":[140],"The":[141],"effectiveness":[142],"our":[144],"demonstrated":[147],"simulations":[149],"two":[151],"cardiological":[152],"sets.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2025-10-10T00:00:00"}