{"id":"https://openalex.org/W4307890626","doi":"https://doi.org/10.1080/00401706.2022.2134928","title":"Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing","display_name":"Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing","publication_year":2022,"publication_date":"2022-10-31","ids":{"openalex":"https://openalex.org/W4307890626","doi":"https://doi.org/10.1080/00401706.2022.2134928"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2022.2134928","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2022.2134928","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","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/A5100386486","display_name":"Xiaoyu Chen","orcid":"https://orcid.org/0000-0002-1870-5290"},"institutions":[{"id":"https://openalex.org/I142740786","display_name":"University of Louisville","ror":"https://ror.org/01ckdn478","country_code":"US","type":"education","lineage":["https://openalex.org/I142740786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoyu Chen","raw_affiliation_strings":["Department of Industrial Engineering, University of Louisville, Louisville, KY"],"raw_orcid":"https://orcid.org/0000-0002-1870-5290","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, University of Louisville, Louisville, KY","institution_ids":["https://openalex.org/I142740786"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003650269","display_name":"Xiaoning Kang","orcid":"https://orcid.org/0000-0003-0394-6240"},"institutions":[{"id":"https://openalex.org/I130480701","display_name":"Dongbei University of Finance and Economics","ror":"https://ror.org/05db1pj03","country_code":"CN","type":"education","lineage":["https://openalex.org/I130480701"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoning Kang","raw_affiliation_strings":["International Business College and Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0003-0394-6240","affiliations":[{"raw_affiliation_string":"International Business College and Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian, China","institution_ids":["https://openalex.org/I130480701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085868833","display_name":"Ran Jin","orcid":"https://orcid.org/0000-0003-3847-4538"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ran Jin","raw_affiliation_strings":["Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003817085","display_name":"Xinwei Deng","orcid":"https://orcid.org/0000-0002-1560-2405"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xinwei Deng","raw_affiliation_strings":["Department of Statistics, Virginia Tech, Blacksburg, VA"],"raw_orcid":"https://orcid.org/0000-0002-1560-2405","affiliations":[{"raw_affiliation_string":"Department of Statistics, Virginia Tech, Blacksburg, VA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003817085"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.9711,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80035849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"65","issue":"2","first_page":"206","last_page":"219"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9929999709129333,"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"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9929999709129333,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9739000201225281,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9681000113487244,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7510113716125488},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5869141221046448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44329044222831726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40303051471710205},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3813725709915161}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7510113716125488},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5869141221046448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44329044222831726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40303051471710205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3813725709915161}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/00401706.2022.2134928","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2022.2134928","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1667817182","https://openalex.org/W1968685950","https://openalex.org/W1973991899","https://openalex.org/W1979026611","https://openalex.org/W1992283888","https://openalex.org/W2001161274","https://openalex.org/W2024153789","https://openalex.org/W2039885242","https://openalex.org/W2045084661","https://openalex.org/W2046422884","https://openalex.org/W2067398275","https://openalex.org/W2067849685","https://openalex.org/W2081905665","https://openalex.org/W2089773781","https://openalex.org/W2122059848","https://openalex.org/W2141086482","https://openalex.org/W2155220505","https://openalex.org/W2311316625","https://openalex.org/W2593657034","https://openalex.org/W2747537200","https://openalex.org/W2765335234","https://openalex.org/W2767448576","https://openalex.org/W2810240646","https://openalex.org/W2890047383","https://openalex.org/W2964219675","https://openalex.org/W2965961020","https://openalex.org/W3017285620","https://openalex.org/W3018141825","https://openalex.org/W3095368355","https://openalex.org/W3101022954","https://openalex.org/W3102666008","https://openalex.org/W4210467306"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4313488044","https://openalex.org/W3209574120","https://openalex.org/W4312192474","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Fog":[0,10,158,170],"manufacturing":[1,6,171],"can":[2],"greatly":[3],"enhance":[4,69],"traditional":[5],"systems":[7],"through":[8],"distributed":[9],"computation":[11],"units,":[12],"which":[13],"are":[14,172],"governed":[15],"by":[16,110],"predictive":[17,32,144],"computational":[18],"workload":[19],"offloading":[20,33],"methods":[21,34],"under":[22],"different":[23],"Industrial":[24],"Internet":[25],"architectures.":[26],"It":[27],"is":[28,108,122],"known":[29],"that":[30],"the":[31,70,79,95,115,126,130,143,157,176,179],"highly":[35],"depend":[36],"on":[37,151],"accurate":[38,138],"prediction":[39,71,155],"and":[40,76,88,154,163],"uncertainty":[41],"quantification":[42],"of":[43,72,98,129,178],"runtime":[44,73,99],"performance":[45,74,100],"metrics,":[46],"containing":[47],"multivariate":[48,65],"mixed-type":[49],"responses":[50,67,107],"(i.e.,":[51],"continuous,":[52],"counting,":[53],"binary).":[54],"In":[55],"this":[56],"work,":[57],"we":[58],"propose":[59],"a":[60,111,119,164,169],"Bayesian":[61],"sparse":[62,127],"regression":[63],"for":[64],"mixed":[66,96],"to":[68,77,92,124,174],"metrics":[75],"enable":[78,125],"statistical":[80,149],"inferences.":[81],"The":[82,102,132],"proposed":[83,133,180],"method":[84,134],"considers":[85],"both":[86],"group":[87],"individual":[89],"variable":[90],"selection":[91],"jointly":[93],"model":[94,113,145,152],"types":[97],"metrics.":[101],"conditional":[103],"dependency":[104],"among":[105],"multiple":[106],"described":[109],"graphical":[112],"using":[114],"precision":[116],"matrix,":[117],"where":[118],"spike-and-slab":[120],"prior":[121],"used":[123],"estimation":[128],"graph.":[131],"not":[135],"only":[136],"achieves":[137],"prediction,":[139],"but":[140],"also":[141],"makes":[142],"more":[146],"interpretable":[147],"with":[148],"inferences":[150],"parameters":[153],"in":[156,168],"manufacturing.":[159],"A":[160],"simulation":[161],"study":[162],"real":[165],"case":[166],"example":[167],"conducted":[173],"demonstrate":[175],"merits":[177],"model.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}