{"id":"https://openalex.org/W2060496771","doi":"https://doi.org/10.1109/tr.2014.2354934","title":"A Hierarchical Bayesian Degradation Model for Heterogeneous Data","display_name":"A Hierarchical Bayesian Degradation Model for Heterogeneous Data","publication_year":2014,"publication_date":"2014-09-10","ids":{"openalex":"https://openalex.org/W2060496771","doi":"https://doi.org/10.1109/tr.2014.2354934","mag":"2060496771"},"language":"en","primary_location":{"id":"doi:10.1109/tr.2014.2354934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tr.2014.2354934","pdf_url":null,"source":{"id":"https://openalex.org/S87725633","display_name":"IEEE Transactions on Reliability","issn_l":"0018-9529","issn":["0018-9529","1558-1721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Reliability","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/A5101813855","display_name":"Tao Yuan","orcid":"https://orcid.org/0000-0003-1791-5736"},"institutions":[{"id":"https://openalex.org/I4210106879","display_name":"Ohio University","ror":"https://ror.org/01jr3y717","country_code":"US","type":"education","lineage":["https://openalex.org/I4210106879"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Yuan","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA","Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA","institution_ids":["https://openalex.org/I4210106879"]},{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA#TAB#","institution_ids":["https://openalex.org/I4210106879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039383904","display_name":"Yizhen Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yizhen Ji","raw_affiliation_strings":["DaVita Inc., Tacoma, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DaVita Inc., Tacoma, WA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9707,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.91422011,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"64","issue":"1","first_page":"63","last_page":"70"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","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"}},"topics":[{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9968000054359436,"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":0.9962000250816345,"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/akaike-information-criterion","display_name":"Akaike information criterion","score":0.7279857993125916},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6471059918403625},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.5317486524581909},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5305141806602478},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5095036625862122},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.45040377974510193},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43764862418174744},{"id":"https://openalex.org/keywords/random-effects-model","display_name":"Random effects model","score":0.43708446621894836},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42177602648735046},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3681636452674866},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3563106060028076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18841132521629333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1771494448184967}],"concepts":[{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.7279857993125916},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6471059918403625},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.5317486524581909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5305141806602478},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5095036625862122},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.45040377974510193},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43764862418174744},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.43708446621894836},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42177602648735046},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3681636452674866},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3563106060028076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18841132521629333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1771494448184967},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C95190672","wikidata":"https://www.wikidata.org/wiki/Q815382","display_name":"Meta-analysis","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tr.2014.2354934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tr.2014.2354934","pdf_url":null,"source":{"id":"https://openalex.org/S87725633","display_name":"IEEE Transactions on Reliability","issn_l":"0018-9529","issn":["0018-9529","1558-1721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Reliability","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.4099999964237213,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W31895810","https://openalex.org/W32980360","https://openalex.org/W71916978","https://openalex.org/W75581641","https://openalex.org/W180142960","https://openalex.org/W1964721292","https://openalex.org/W1967495697","https://openalex.org/W1974315578","https://openalex.org/W1981100514","https://openalex.org/W1989964704","https://openalex.org/W1993147091","https://openalex.org/W1998967412","https://openalex.org/W2009048088","https://openalex.org/W2033828039","https://openalex.org/W2038885294","https://openalex.org/W2045656233","https://openalex.org/W2050009269","https://openalex.org/W2057765075","https://openalex.org/W2058815839","https://openalex.org/W2059279601","https://openalex.org/W2064494361","https://openalex.org/W2069883713","https://openalex.org/W2072623282","https://openalex.org/W2082503527","https://openalex.org/W2084254889","https://openalex.org/W2090749763","https://openalex.org/W2100736366","https://openalex.org/W2100753277","https://openalex.org/W2135966495","https://openalex.org/W2151038992","https://openalex.org/W2160118356","https://openalex.org/W2161015681","https://openalex.org/W2168175751","https://openalex.org/W2169281412","https://openalex.org/W2364303559","https://openalex.org/W2409895065","https://openalex.org/W3037265734","https://openalex.org/W4232383088","https://openalex.org/W4239218596","https://openalex.org/W4245666426","https://openalex.org/W4255582690","https://openalex.org/W4299419703","https://openalex.org/W6665272994","https://openalex.org/W6707414681"],"related_works":["https://openalex.org/W2323909351","https://openalex.org/W2272944977","https://openalex.org/W203054622","https://openalex.org/W1567296366","https://openalex.org/W4318457042","https://openalex.org/W2183628556","https://openalex.org/W4213439144","https://openalex.org/W1991247336","https://openalex.org/W2022240706","https://openalex.org/W53217798"],"abstract_inverted_index":{"Degradation":[0],"data":[1],"may":[2],"be":[3],"collected":[4],"from":[5],"a":[6,18,39,58,64],"population":[7],"with":[8],"heterogeneous":[9,27],"subpopulations.":[10,90],"This":[11],"paper":[12],"contributes":[13],"to":[14,96],"the":[15,32,47,51,87,98,101,114],"development":[16],"of":[17,89,100],"new":[19],"statistical":[20],"modeling":[21],"and":[22,37,81],"computation":[23],"method":[24],"for":[25,46,72,85],"analyzing":[26],"degradation":[28,34,42,115],"data.":[29,116],"We":[30,62],"adopt":[31],"random-coefficient":[33],"path":[35],"approach,":[36],"propose":[38],"hierarchical":[40],"Bayesian":[41],"model.":[43,61],"To":[44],"account":[45],"heterogeneity,":[48],"we":[49],"model":[50],"unit-to-unit":[52],"variability":[53],"via":[54],"random":[55],"parameters":[56],"in":[57,113],"Gaussian":[59],"mixture":[60],"developed":[63],"computationally":[65],"convenient":[66],"algorithm":[67],"that":[68,107],"combines":[69],"Gibbs":[70],"sampling":[71],"parameter":[73],"estimation":[74],"as":[75,77],"well":[76],"failure-time":[78],"distribution":[79],"prediction":[80],"Akaike":[82],"information":[83],"criterion":[84],"determining":[86],"number":[88],"A":[91],"numerical":[92],"example":[93],"is":[94],"used":[95],"illustrate":[97],"advantages":[99],"proposed":[102],"methodology":[103],"over":[104],"existing":[105],"methods":[106],"do":[108],"not":[109],"explicitly":[110],"consider":[111],"heterogeneity":[112]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
