{"id":"https://openalex.org/W4405515504","doi":"https://doi.org/10.1080/10618600.2024.2441165","title":"AutoGFI: Streamlined Generalized Fiducial Inference for Modern Inference Problems in Models with Additive Errors","display_name":"AutoGFI: Streamlined Generalized Fiducial Inference for Modern Inference Problems in Models with Additive Errors","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4405515504","doi":"https://doi.org/10.1080/10618600.2024.2441165"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2024.2441165","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2441165","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2441165?download=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2441165?download=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059747977","display_name":"Wei Du","orcid":"https://orcid.org/0000-0001-9872-4821"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Du","raw_affiliation_strings":["Department of Statistics, University of California","Department of Statistics, University of California, Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California","institution_ids":[]},{"raw_affiliation_string":"Department of Statistics, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084746497","display_name":"Jan Hannig","orcid":"https://orcid.org/0000-0002-4164-0173"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jan Hannig","raw_affiliation_strings":["Department of Statistics and Operations Research, University of North Carolina at Chapel Hill"],"raw_orcid":"https://orcid.org/0000-0002-4164-0173","affiliations":[{"raw_affiliation_string":"Department of Statistics and Operations Research, University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049635087","display_name":"Thomas C. M. Lee","orcid":"https://orcid.org/0000-0001-7067-405X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thomas C. M. Lee","raw_affiliation_strings":["Department of Statistics, University of California","Department of Statistics, University of California, Davis"],"raw_orcid":"https://orcid.org/0000-0001-7067-405X","affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California","institution_ids":[]},{"raw_affiliation_string":"Department of Statistics, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110394340","display_name":"Yi Su","orcid":"https://orcid.org/0009-0002-5580-0075"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Su","raw_affiliation_strings":["Department of Statistics, University of California","Department of Statistics, University of California, Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California","institution_ids":[]},{"raw_affiliation_string":"Department of Statistics, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066876830","display_name":"Chunzhe Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chunzhe Zhang","raw_affiliation_strings":["Department of Statistics, University of California","Department of Statistics, University of California, Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California","institution_ids":[]},{"raw_affiliation_string":"Department of Statistics, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049635087"],"corresponding_institution_ids":["https://openalex.org/I84218800"],"apc_list":null,"apc_paid":null,"fwci":0.4588,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62939432,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"34","issue":"4","first_page":"1214","last_page":"1225"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9934999942779541,"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/inference","display_name":"Inference","score":0.7861649990081787},{"id":"https://openalex.org/keywords/fiducial-marker","display_name":"Fiducial marker","score":0.7312541007995605},{"id":"https://openalex.org/keywords/fiducial-inference","display_name":"Fiducial inference","score":0.7116037607192993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5160643458366394},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5118011832237244},{"id":"https://openalex.org/keywords/generalized-additive-model","display_name":"Generalized additive model","score":0.5034179091453552},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"Frequentist inference","score":0.4741458594799042},{"id":"https://openalex.org/keywords/predictive-inference","display_name":"Predictive inference","score":0.42890089750289917},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39814522862434387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.384101003408432},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.3714233636856079},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35307762026786804},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1957419216632843}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7861649990081787},{"id":"https://openalex.org/C173974348","wikidata":"https://www.wikidata.org/wiki/Q1469893","display_name":"Fiducial marker","level":2,"score":0.7312541007995605},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.7116037607192993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5160643458366394},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5118011832237244},{"id":"https://openalex.org/C194648359","wikidata":"https://www.wikidata.org/wiki/Q3318054","display_name":"Generalized additive model","level":2,"score":0.5034179091453552},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.4741458594799042},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.42890089750289917},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39814522862434387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.384101003408432},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3714233636856079},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35307762026786804},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1957419216632843}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2024.2441165","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2441165","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2441165?download=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1080/10618600.2024.2441165","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2441165","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2441165?download=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"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":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.4399999976158142,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2045930519","display_name":"DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers for and from Emerging Astronomical Data Challenges","funder_award_id":"2113605","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2673924751","display_name":null,"funder_award_id":"DMS-1916125","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3069504102","display_name":"HDR TRIPODS: UC Davis TETRAPODS Institute of Data Science","funder_award_id":"1934568","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3685830080","display_name":null,"funder_award_id":"DMS-2113404","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4046975250","display_name":null,"funder_award_id":"DMS-2210337","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4595972479","display_name":null,"funder_award_id":"DMS-2113605","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5652558899","display_name":"Data Integration Via Analysis of Subspaces (DIVAS)","funder_award_id":"2113404","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G565295993","display_name":null,"funder_award_id":"CCF-1934568","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G645903391","display_name":"Collaborative Research: Emerging Variants of Generalized Fiducial Inference","funder_award_id":"2210388","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8010188014","display_name":"Collaborative Research: Generalized Fiducial Inference in the Age of Data Science","funder_award_id":"1916125","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8199172555","display_name":null,"funder_award_id":"DMS-2210388","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405515504.pdf","grobid_xml":"https://content.openalex.org/works/W4405515504.grobid-xml"},"referenced_works_count":71,"referenced_works":["https://openalex.org/W88652374","https://openalex.org/W1580176742","https://openalex.org/W1757639814","https://openalex.org/W1970176196","https://openalex.org/W1970948570","https://openalex.org/W1976618413","https://openalex.org/W1978838225","https://openalex.org/W1986650169","https://openalex.org/W1988060518","https://openalex.org/W1995945562","https://openalex.org/W1996399172","https://openalex.org/W2008156454","https://openalex.org/W2024165284","https://openalex.org/W2037594831","https://openalex.org/W2047071281","https://openalex.org/W2047415626","https://openalex.org/W2053565514","https://openalex.org/W2057049867","https://openalex.org/W2069870615","https://openalex.org/W2078194248","https://openalex.org/W2078411132","https://openalex.org/W2084840427","https://openalex.org/W2111297856","https://openalex.org/W2116416291","https://openalex.org/W2116680824","https://openalex.org/W2119367754","https://openalex.org/W2136002544","https://openalex.org/W2140773653","https://openalex.org/W2144487656","https://openalex.org/W2144730813","https://openalex.org/W2145962650","https://openalex.org/W2159307722","https://openalex.org/W2160278489","https://openalex.org/W2162156946","https://openalex.org/W2166163936","https://openalex.org/W2167958905","https://openalex.org/W2169379894","https://openalex.org/W2186878252","https://openalex.org/W2236536336","https://openalex.org/W2316089952","https://openalex.org/W2326809886","https://openalex.org/W2511429843","https://openalex.org/W2521607924","https://openalex.org/W2611328865","https://openalex.org/W2899971035","https://openalex.org/W2946854915","https://openalex.org/W2952113380","https://openalex.org/W2962769133","https://openalex.org/W2963114672","https://openalex.org/W2963847248","https://openalex.org/W2965198381","https://openalex.org/W2982569731","https://openalex.org/W3005611649","https://openalex.org/W3039880960","https://openalex.org/W3094857757","https://openalex.org/W3099056632","https://openalex.org/W3099550161","https://openalex.org/W3106496672","https://openalex.org/W3120081705","https://openalex.org/W4249847358","https://openalex.org/W4250589301","https://openalex.org/W4280530019","https://openalex.org/W4282983592","https://openalex.org/W4320150047","https://openalex.org/W4403520521","https://openalex.org/W6684295950","https://openalex.org/W6700363712","https://openalex.org/W6703949738","https://openalex.org/W6728790633","https://openalex.org/W6734458846","https://openalex.org/W6769185628"],"related_works":["https://openalex.org/W4235379902","https://openalex.org/W4235385816","https://openalex.org/W2478683457","https://openalex.org/W4251525937","https://openalex.org/W1996865119","https://openalex.org/W4242294605","https://openalex.org/W2157736211","https://openalex.org/W2764025266","https://openalex.org/W4239322872","https://openalex.org/W3022940228"],"abstract_inverted_index":{"The":[0],"concept":[1],"of":[2,19,64,103,134,172],"fiducial":[3,36,50,56],"inference":[4,51,57,72,112,181],"was":[5],"introduced":[6],"by":[7,81],"R.":[8],"A.":[9],"Fisher":[10],"in":[11,29,44,49,52,119,174],"the":[12,16,23,53,170,179],"1930s":[13],"to":[14,41,106,144,168],"address":[15],"perceived":[17],"limitations":[18],"Bayesian":[20],"inference,":[21],"particularly":[22],"need":[24],"for":[25,70,130],"subjective":[26],"prior":[27,33],"distributions":[28],"cases":[30],"with":[31,114],"limited":[32],"information.However,":[34],"Fisher's":[35,65],"approach":[37],"lost":[38],"favor":[39],"due":[40],"complications,":[42],"especially":[43],"multiparameter":[45],"problems.With":[46],"renewed":[47],"interest":[48],"2000s,":[54],"generalized":[55],"(GFI)":[58],"emerged":[59],"as":[60,90],"a":[61,100,123,127,131],"promising":[62],"extension":[63],"ideas,":[66],"offering":[67],"new":[68],"solutions":[69],"complex":[71,86],"challenges.Despite":[73],"its":[74,108,120,139,166],"potential,":[75],"GFI's":[76],"adoption":[77],"has":[78],"been":[79],"hindered":[80],"demanding":[82],"mathematical":[83],"derivations":[84],"and":[85,136,152],"implementation":[87],"requirements,":[88],"such":[89],"Markov":[91],"chain":[92],"Monte":[93],"Carlo":[94],"(MCMC)":[95],"algorithms.This":[96],"article":[97],"introduces":[98],"AutoGFI,":[99],"streamlined":[101],"variant":[102],"GFI":[104,173],"designed":[105],"simplify":[107],"application":[109,171],"across":[110],"various":[111],"problems":[113],"additive":[115],"noise.AutoGFI's":[116],"accessibility":[117],"lies":[118],"simplicity-requiring":[121],"only":[122],"fitting":[124],"routine-making":[125],"it":[126],"feasible":[128],"option":[129],"wider":[132],"range":[133],"researchers":[135],"practitioners.To":[137],"demonstrate":[138],"efficacy,":[140],"AutoGFI":[141],"is":[142],"applied":[143],"three":[145],"challenging":[146],"problems:":[147],"tensor":[148],"regression,":[149],"matrix":[150],"completion,":[151],"network":[153],"cohesion":[154],"regression.These":[155],"case":[156],"studies":[157],"showcase":[158],"AutoGFI's":[159],"competitive":[160],"performance":[161],"against":[162],"specialized":[163],"solutions,":[164],"highlighting":[165],"potential":[167],"broaden":[169],"practical":[175],"domains,":[176],"ultimately":[177],"enriching":[178],"statistical":[180],"toolkit.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}