{"id":"https://openalex.org/W2912397373","doi":"https://doi.org/10.1080/03610918.2018.1484477","title":"Likelihood-based quantile mixed effects models for longitudinal data with multiple features via MCEM algorithm","display_name":"Likelihood-based quantile mixed effects models for longitudinal data with multiple features via MCEM algorithm","publication_year":2019,"publication_date":"2019-02-10","ids":{"openalex":"https://openalex.org/W2912397373","doi":"https://doi.org/10.1080/03610918.2018.1484477","mag":"2912397373"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2018.1484477","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1484477","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/A5079518086","display_name":"Yuzhu Tian","orcid":"https://orcid.org/0000-0002-6173-0985"},"institutions":[{"id":"https://openalex.org/I137867983","display_name":"Central University of Finance and Economics","ror":"https://ror.org/008e3hf02","country_code":"CN","type":"education","lineage":["https://openalex.org/I137867983"]},{"id":"https://openalex.org/I167383011","display_name":"Henan University of Science and Technology","ror":"https://ror.org/05d80kz58","country_code":"CN","type":"education","lineage":["https://openalex.org/I167383011"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuzhu Tian","raw_affiliation_strings":["School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China;","School of Statistics and Mathematics, The Central University of Finance and Economics, Beijing, China;"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China;","institution_ids":["https://openalex.org/I167383011"]},{"raw_affiliation_string":"School of Statistics and Mathematics, The Central University of Finance and Economics, Beijing, China;","institution_ids":["https://openalex.org/I137867983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051577475","display_name":"Liyong Wang","orcid":"https://orcid.org/0000-0002-0527-7063"},"institutions":[{"id":"https://openalex.org/I137867983","display_name":"Central University of Finance and Economics","ror":"https://ror.org/008e3hf02","country_code":"CN","type":"education","lineage":["https://openalex.org/I137867983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liyong Wang","raw_affiliation_strings":["School of Statistics and Mathematics, The Central University of Finance and Economics, Beijing, China;"],"affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, The Central University of Finance and Economics, Beijing, China;","institution_ids":["https://openalex.org/I137867983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069557245","display_name":"Man\u2010Lai Tang","orcid":"https://orcid.org/0000-0001-6460-8136"},"institutions":[{"id":"https://openalex.org/I47605537","display_name":"The Hang Seng University of Hong Kong","ror":"https://ror.org/04fa64g55","country_code":"CN","type":"education","lineage":["https://openalex.org/I47605537"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Manlai Tang","raw_affiliation_strings":["Department of Mathematics and Statistics, Hang Seng Management College, Hong Kong;"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Hang Seng Management College, Hong Kong;","institution_ids":["https://openalex.org/I47605537"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081970358","display_name":"Maozai Tian","orcid":"https://orcid.org/0000-0002-0515-4477"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maozai Tian","raw_affiliation_strings":["School of Statistics, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Statistics, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5079518086"],"corresponding_institution_ids":["https://openalex.org/I137867983","https://openalex.org/I167383011"],"apc_list":null,"apc_paid":null,"fwci":0.4682,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.63456659,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"49","issue":"2","first_page":"317","last_page":"334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9993000030517578,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/censoring","display_name":"Censoring (clinical trials)","score":0.6888679265975952},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6199935674667358},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6062434911727905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.580400824546814},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.510826826095581},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.4749227464199066},{"id":"https://openalex.org/keywords/random-effects-model","display_name":"Random effects model","score":0.4689706563949585},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.45546919107437134},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4304054379463196},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4218537211418152},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4174477458000183},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4133182764053345},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3944095969200134},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34950822591781616},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3033366799354553},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2006245255470276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18200263381004333},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.13155052065849304}],"concepts":[{"id":"https://openalex.org/C137668524","wikidata":"https://www.wikidata.org/wiki/Q189813","display_name":"Censoring (clinical trials)","level":2,"score":0.6888679265975952},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6199935674667358},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6062434911727905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.580400824546814},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.510826826095581},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.4749227464199066},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.4689706563949585},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.45546919107437134},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4304054379463196},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4218537211418152},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4174477458000183},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4133182764053345},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3944095969200134},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34950822591781616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3033366799354553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2006245255470276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18200263381004333},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.13155052065849304},{"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2018.1484477","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2018.1484477","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":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5}],"awards":[{"id":"https://openalex.org/G5122393514","display_name":null,"funder_award_id":"2017M610156","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6678007391","display_name":null,"funder_award_id":"11501167","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"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W381804301","https://openalex.org/W569613068","https://openalex.org/W1490768654","https://openalex.org/W1559239896","https://openalex.org/W1564830971","https://openalex.org/W1579925870","https://openalex.org/W1865687278","https://openalex.org/W1894690275","https://openalex.org/W1981090284","https://openalex.org/W1981463702","https://openalex.org/W1988018581","https://openalex.org/W1995945562","https://openalex.org/W1998073888","https://openalex.org/W2003432789","https://openalex.org/W2012532075","https://openalex.org/W2024544539","https://openalex.org/W2041580290","https://openalex.org/W2045367611","https://openalex.org/W2059416780","https://openalex.org/W2062380248","https://openalex.org/W2073464636","https://openalex.org/W2076881231","https://openalex.org/W2077971602","https://openalex.org/W2078722674","https://openalex.org/W2082246284","https://openalex.org/W2084007633","https://openalex.org/W2084871407","https://openalex.org/W2086020235","https://openalex.org/W2095072993","https://openalex.org/W2097168082","https://openalex.org/W2121841926","https://openalex.org/W2121956497","https://openalex.org/W2138198368","https://openalex.org/W2142326648","https://openalex.org/W2155372200","https://openalex.org/W2158487021","https://openalex.org/W2161896685","https://openalex.org/W2170395887","https://openalex.org/W2172144962","https://openalex.org/W2346148797","https://openalex.org/W2511964807","https://openalex.org/W2538002598","https://openalex.org/W2606555739","https://openalex.org/W2621093274","https://openalex.org/W3105706340","https://openalex.org/W3106889297","https://openalex.org/W4241653265","https://openalex.org/W4255619213","https://openalex.org/W4256684123","https://openalex.org/W4302338619"],"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":{"In":[0,26,151],"longitudinal":[1,66,126,162],"data":[2,5,84,127,130,171,204],"analysis,":[3],"many":[4],"features":[6,172],"are":[7,15,199],"frequently":[8],"encountered":[9],"in":[10,76,108],"practice.":[11],"For":[12],"example,":[13],"variates":[14],"usually":[16],"measured":[17],"with":[18,69,128],"censoring,":[19,72],"substantial":[20],"errors":[21,75],"and":[22,78,145,201],"non-normal":[23,116],"feature,":[24],"etc.":[25],"such":[27],"case,":[28],"it":[29,59],"is":[30,57,187,206],"impossible":[31],"to":[32,124,189,208],"give":[33],"a":[34,49,62,135],"precise":[35],"result":[36,107],"by":[37,81,165],"conducting":[38],"statistical":[39],"inference":[40],"of":[41,54,89,99,138,142],"separate":[42],"analysis.":[43],"The":[44,52],"joint":[45,55,158],"modeling":[46,56,91],"may":[47,106],"be":[48],"considerable":[50],"alternative.":[51],"superiority":[53],"that":[58],"can":[60,133],"implement":[61],"simultaneous":[63],"analysis":[64,205],"for":[65,161,167],"mixed":[67,163],"models":[68,164],"random":[70],"effects,":[71],"nor-normal":[73],"errors,":[74],"covariates":[77],"heavy-tailed":[79],"feature":[80],"pooling":[82],"all":[83],"information":[85],"together.":[86],"However,":[87],"most":[88],"traditional":[90],"methods":[92],"aim":[93],"at":[94],"depicting":[95],"the":[96,139,156,168,176,181,191,210],"average":[97],"variation":[98],"outcome":[100,143],"variable":[101,144],"conditionally":[102],"on":[103,175],"covariates,":[104],"which":[105,132],"non-robust":[109],"estimation":[110,149,192],"results":[111],"when":[112],"suffering":[113],"outliers":[114],"or":[115],"errors.":[117],"Quantile":[118],"regression":[119,160],"provides":[120],"an":[121,202],"attractive":[122],"alternative":[123],"model":[125],"multiple":[129,170],"features,":[131],"draw":[134],"complete":[136],"picture":[137],"conditional":[140],"distributions":[141],"bring":[146],"out":[147],"robust":[148],"results.":[150],"this":[152],"paper,":[153],"we":[154],"conduct":[155],"likelihood-based":[157],"quantile":[159],"accounting":[166],"above":[169],"simultaneously.":[173],"Based":[174],"asymmetric":[177],"Laplace":[178],"distribution":[179],"(ALD),":[180],"Monte":[182,196],"Carlo":[183,197],"Expectation-Maximization":[184],"(MCEM)":[185],"algorithm":[186],"employed":[188],"address":[190],"problem.":[193],"Finally,":[194],"some":[195],"simulations":[198],"conducted":[200],"AIDS":[203],"provided":[207],"illustrate":[209],"developed":[211],"procedures.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}