{"id":"https://openalex.org/W4398235688","doi":"https://doi.org/10.3390/a17060224","title":"Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo","display_name":"Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo","publication_year":2024,"publication_date":"2024-05-23","ids":{"openalex":"https://openalex.org/W4398235688","doi":"https://doi.org/10.3390/a17060224"},"language":"en","primary_location":{"id":"doi:10.3390/a17060224","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a17060224","pdf_url":"https://www.mdpi.com/1999-4893/17/6/224/pdf?version=1716471464","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/17/6/224/pdf?version=1716471464","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024227819","display_name":"Hassan Hachem","orcid":"https://orcid.org/0009-0007-5876-853X"},"institutions":[{"id":"https://openalex.org/I22248866","display_name":"AgroParisTech","ror":"https://ror.org/02kbmgc12","country_code":"FR","type":"education","lineage":["https://openalex.org/I22248866","https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4210088668","display_name":"Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement","ror":"https://ror.org/003vg9w96","country_code":"FR","type":"government","lineage":["https://openalex.org/I4210088668"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hassan Hachem","raw_affiliation_strings":["INRAE, UR1204, AgroParisTech, 147 Rue de l\u2019Universit\u00e9, 75338 Paris, France"],"raw_orcid":"https://orcid.org/0009-0007-5876-853X","affiliations":[{"raw_affiliation_string":"INRAE, UR1204, AgroParisTech, 147 Rue de l\u2019Universit\u00e9, 75338 Paris, France","institution_ids":["https://openalex.org/I22248866","https://openalex.org/I4210088668"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057051050","display_name":"Candy Abboud","orcid":"https://orcid.org/0000-0002-5453-2577"},"institutions":[{"id":"https://openalex.org/I2803079837","display_name":"American University of the Middle East","ror":"https://ror.org/02gqgne03","country_code":"KW","type":"education","lineage":["https://openalex.org/I2803079837"]}],"countries":["KW"],"is_corresponding":true,"raw_author_name":"Candy Abboud","raw_affiliation_strings":["College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait","College of Engineering and Technology, American University of the Middle East, Egaila 15453, Kuwait"],"raw_orcid":"https://orcid.org/0000-0002-5453-2577","affiliations":[{"raw_affiliation_string":"College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait","institution_ids":["https://openalex.org/I2803079837"]},{"raw_affiliation_string":"College of Engineering and Technology, American University of the Middle East, Egaila 15453, Kuwait","institution_ids":["https://openalex.org/I2803079837"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057051050"],"corresponding_institution_ids":["https://openalex.org/I2803079837"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1782},"fwci":0.5082,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63883753,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"17","issue":"6","first_page":"224","last_page":"224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9987000226974487,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9987000226974487,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9984999895095825,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9919000267982483,"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/quantile","display_name":"Quantile","score":0.7333380579948425},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.7108908295631409},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5873725414276123},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.5684167146682739},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.5553433895111084},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.5273013114929199},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.43250900506973267},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.42918381094932556},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4291417598724365},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4284699559211731},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41989997029304504},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.4165247678756714},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4062662720680237},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.29006797075271606},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14119753241539001}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.7333380579948425},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7108908295631409},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5873725414276123},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.5684167146682739},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.5553433895111084},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.5273013114929199},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.43250900506973267},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.42918381094932556},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4291417598724365},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4284699559211731},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41989997029304504},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.4165247678756714},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4062662720680237},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29006797075271606},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14119753241539001}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/a17060224","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a17060224","pdf_url":"https://www.mdpi.com/1999-4893/17/6/224/pdf?version=1716471464","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ed102fc528464791bd2cbe1483688593","is_oa":true,"landing_page_url":"https://doaj.org/article/ed102fc528464791bd2cbe1483688593","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 17, Iss 6, p 224 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/a17060224","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a17060224","pdf_url":"https://www.mdpi.com/1999-4893/17/6/224/pdf?version=1716471464","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4398235688.pdf"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W114051671","https://openalex.org/W1975286873","https://openalex.org/W1981205118","https://openalex.org/W1998073888","https://openalex.org/W2004412581","https://openalex.org/W2036787087","https://openalex.org/W2043436310","https://openalex.org/W2059448777","https://openalex.org/W2065742895","https://openalex.org/W2084177755","https://openalex.org/W2084871407","https://openalex.org/W2095247603","https://openalex.org/W2102360969","https://openalex.org/W2112231177","https://openalex.org/W2133596638","https://openalex.org/W2167239677","https://openalex.org/W2172144962","https://openalex.org/W2283543424","https://openalex.org/W2314022315","https://openalex.org/W2560781390","https://openalex.org/W2577537660","https://openalex.org/W2581839231","https://openalex.org/W2586258055","https://openalex.org/W2780675744","https://openalex.org/W2791308678","https://openalex.org/W2810041204","https://openalex.org/W2945002008","https://openalex.org/W2946130033","https://openalex.org/W2946922323","https://openalex.org/W2954040150","https://openalex.org/W2956075544","https://openalex.org/W2962703949","https://openalex.org/W2963977107","https://openalex.org/W3157968023","https://openalex.org/W3173336494","https://openalex.org/W3193583132","https://openalex.org/W3196930063","https://openalex.org/W3204929893","https://openalex.org/W4206419526","https://openalex.org/W4241385190","https://openalex.org/W4246464108","https://openalex.org/W4252930359","https://openalex.org/W4367295890","https://openalex.org/W4387779674","https://openalex.org/W6604622217","https://openalex.org/W6638674303","https://openalex.org/W6678765805","https://openalex.org/W6679529799","https://openalex.org/W6680518675","https://openalex.org/W6765810662","https://openalex.org/W6823589455","https://openalex.org/W6834913754"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"The":[0,125],"simultaneous":[1],"estimation":[2],"of":[3,16,86,103,113],"multiple":[4],"quantiles":[5,85],"is":[6,72],"a":[7,13,29,44,91],"crucial":[8],"statistical":[9],"task":[10],"that":[11,97],"enables":[12],"thorough":[14],"understanding":[15],"data":[17],"distribution":[18,41],"for":[19,47],"robust":[20],"analysis":[21],"and":[22,93,137,139,148],"decision-making.":[23],"In":[24],"this":[25,34,87],"study,":[26],"we":[27,89,109],"adopt":[28],"Bayesian":[30,95],"approach":[31,115],"to":[32,74,99,141],"tackle":[33],"critical":[35],"task,":[36],"employing":[37],"the":[38,54,62,69,80,100,111,122],"asymmetric":[39],"Laplace":[40],"(ALD)":[42],"as":[43],"flexible":[45],"framework":[46],"quantile":[48,117],"modeling.":[49],"Our":[50],"methodology":[51],"implementation":[52],"involves":[53],"Hamiltonian":[55],"Monte":[56],"Carlo":[57],"(HMC)":[58],"algorithm,":[59],"building":[60],"on":[61,79],"foundation":[63],"laid":[64],"in":[65,116,132,151],"prior":[66],"work,":[67],"where":[68],"error":[70],"term":[71],"assumed":[73],"follow":[75],"an":[76],"ALD.":[77],"Capitalizing":[78],"interplay":[81],"between":[82],"two":[83],"distinct":[84],"distribution,":[88],"endorse":[90],"straightforward":[92],"fully":[94],"method":[96,127],"adheres":[98],"non-crossing":[101],"property":[102],"quantiles.":[104],"Illustrated":[105],"through":[106],"simulated":[107],"scenarios,":[108],"showcase":[110],"effectiveness":[112],"our":[114],"estimation,":[118],"enhancing":[119,149],"precision":[120],"via":[121],"HMC":[123],"algorithm.":[124],"proposed":[126],"proves":[128],"versatile,":[129],"finding":[130],"application":[131],"finance,":[133],"environmental":[134],"science,":[135],"healthcare,":[136],"manufacturing,":[138],"contributing":[140],"sustainable":[142],"development":[143],"goals":[144],"by":[145],"fostering":[146],"innovation":[147],"decision-making":[150],"diverse":[152],"fields.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}