{"id":"https://openalex.org/W3193695871","doi":"https://doi.org/10.1080/03610918.2021.1951759","title":"Regression estimation of the marginal models with general relative risk form for multivariate failure time data","display_name":"Regression estimation of the marginal models with general relative risk form for multivariate failure time data","publication_year":2021,"publication_date":"2021-07-20","ids":{"openalex":"https://openalex.org/W3193695871","doi":"https://doi.org/10.1080/03610918.2021.1951759","mag":"3193695871"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.1951759","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1951759","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/A5101794178","display_name":"Feifei Yan","orcid":"https://orcid.org/0000-0001-9647-5593"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feifei Yan","raw_affiliation_strings":["School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0000-0001-9647-5593","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072881452","display_name":"Lin Zhu","orcid":"https://orcid.org/0000-0002-7826-3260"},"institutions":[{"id":"https://openalex.org/I4210119674","display_name":"East China University of Technology","ror":"https://ror.org/027385r44","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lin Zhu","raw_affiliation_strings":["School of Science, East China University of Technology, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0000-0002-7826-3260","affiliations":[{"raw_affiliation_string":"School of Science, East China University of Technology, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I4210119674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072881452"],"corresponding_institution_ids":["https://openalex.org/I4210119674"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12537073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"52","issue":"8","first_page":"3958","last_page":"3972"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9986000061035156,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9864000082015991,"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/mathematics","display_name":"Mathematics","score":0.6171165704727173},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.585456907749176},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5198876857757568},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.48831865191459656},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4655507206916809},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.44841331243515015},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4387586712837219},{"id":"https://openalex.org/keywords/marginal-model","display_name":"Marginal model","score":0.43287843465805054},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3529336750507355},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.2588339149951935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.24803170561790466}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6171165704727173},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.585456907749176},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5198876857757568},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.48831865191459656},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4655507206916809},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.44841331243515015},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4387586712837219},{"id":"https://openalex.org/C197656967","wikidata":"https://www.wikidata.org/wiki/Q17058458","display_name":"Marginal model","level":3,"score":0.43287843465805054},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3529336750507355},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2588339149951935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.24803170561790466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2021.1951759","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1951759","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":[{"score":0.41999998688697815,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G1032401707","display_name":"\u591a\u91cd\u7ea6\u675f\u4e0b\u76c6\u5730\u6df1\u90e8\u70ed\u7269\u6027\u53c2\u6570\u53cd\u6f14\u7814\u7a76-\u4ee5\u743c\u4e1c\u5357\u76c6\u5730\u4e3a\u4f8b","funder_award_id":"41766001","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W67376296","https://openalex.org/W1958271975","https://openalex.org/W1969815525","https://openalex.org/W1980383444","https://openalex.org/W1991333308","https://openalex.org/W1998543014","https://openalex.org/W2003603025","https://openalex.org/W2017513884","https://openalex.org/W2024106288","https://openalex.org/W2032082017","https://openalex.org/W2034948055","https://openalex.org/W2047586710","https://openalex.org/W2084626053","https://openalex.org/W2085207239","https://openalex.org/W2092648747","https://openalex.org/W2328015960","https://openalex.org/W2460591733","https://openalex.org/W2582743139","https://openalex.org/W2795680774"],"related_works":["https://openalex.org/W4287880334","https://openalex.org/W4366700029","https://openalex.org/W4285230481","https://openalex.org/W2057903007","https://openalex.org/W3138308052","https://openalex.org/W4300899548","https://openalex.org/W2950535105","https://openalex.org/W1967411053","https://openalex.org/W1596518867","https://openalex.org/W2166774298"],"abstract_inverted_index":{"We":[0],"develop":[1],"the":[2,8,12,75,80,91,95,98,102,109,112,116,126,129,146],"quadratic":[3,76],"inference":[4,77,82],"function":[5,24,78],"method":[6,93,110,136],"to":[7,38,53],"parameter":[9,81],"estimation":[10,103],"of":[11,141],"marginal":[13],"models":[14],"with":[15,59,125],"general":[16],"relative":[17,33],"risk":[18,34],"form":[19,35],"and":[20,56,121],"common":[21],"baseline":[22],"hazard":[23],"for":[25,79],"multivariate":[26],"failure":[27,99],"time":[28],"data.":[29],"The":[30,44,86,134],"usual":[31],"exponential":[32],"is":[36,51,132,137],"relaxed":[37],"an":[39],"non-negative":[40],"twice":[41],"differentiable":[42],"form.":[43],"proposed":[45,92,135],"estimator,":[46],"under":[47],"some":[48],"regularity":[49],"conditions,":[50],"shown":[52],"be":[54,65,119],"consistent":[55],"asymptotically":[57],"normal":[58],"a":[60,70,142],"covariance":[61],"matrix":[62],"that":[63,90],"can":[64,122],"consistently":[66],"estimated.":[67],"In":[68],"addition,":[69],"test":[71],"statistic":[72],"based":[73],"on":[74],"has":[83],"been":[84],"provided.":[85],"simulation":[87],"results":[88],"show":[89],"taking":[94],"correlation":[96,117],"between":[97],"times":[100],"into":[101],"procedure":[104],"gains":[105],"more":[106],"efficiency":[107],"than":[108],"using":[111],"independent":[113],"structure":[114],"when":[115,128],"cannot":[118],"ignored,":[120],"easily":[123],"deal":[124],"situation":[127],"cluster":[130],"size":[131],"large.":[133],"illustrated":[138],"by":[139],"analysis":[140],"real":[143],"data":[144],"from":[145],"Diabetic":[147],"Retinopathy":[148],"Study.":[149]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}