{"id":"https://openalex.org/W2912403745","doi":"https://doi.org/10.1080/03610918.2019.1572763","title":"Estimation for frailty measurement error Cox models based on profile likelihood and Bayes methods","display_name":"Estimation for frailty measurement error Cox models based on profile likelihood and Bayes methods","publication_year":2019,"publication_date":"2019-02-10","ids":{"openalex":"https://openalex.org/W2912403745","doi":"https://doi.org/10.1080/03610918.2019.1572763","mag":"2912403745"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1572763","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1572763","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/A5071549343","display_name":"Caiya Zhang","orcid":"https://orcid.org/0000-0003-3773-4027"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caiya Zhang","raw_affiliation_strings":["Department of Statistics, Zhejiang University City College, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Zhejiang University City College, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063471251","display_name":"Xiaolu Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaolu Gu","raw_affiliation_strings":["Department of Statistics, Zhejiang University City College, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Zhejiang University City College, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100692978","display_name":"Yingyu Chen","orcid":"https://orcid.org/0000-0003-1243-3607"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yingyu Chen","raw_affiliation_strings":["Department of Statistics, Zhejiang University City College, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Zhejiang University City College, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5100692978"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.01496764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"50","issue":"4","first_page":"1025","last_page":"1038"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9972000122070312,"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/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.7711300849914551},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.737586259841919},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7205820083618164},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6800937652587891},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6165593862533569},{"id":"https://openalex.org/keywords/observational-error","display_name":"Observational error","score":0.5434179902076721},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.46962833404541016},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4447348117828369},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.42705368995666504},{"id":"https://openalex.org/keywords/hazard","display_name":"Hazard","score":0.41973596811294556},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.41657572984695435},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4141744077205658},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37788745760917664},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1659073531627655},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.08640298247337341}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7711300849914551},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.737586259841919},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7205820083618164},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6800937652587891},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6165593862533569},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.5434179902076721},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.46962833404541016},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4447348117828369},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.42705368995666504},{"id":"https://openalex.org/C49261128","wikidata":"https://www.wikidata.org/wiki/Q1132455","display_name":"Hazard","level":2,"score":0.41973596811294556},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.41657572984695435},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4141744077205658},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37788745760917664},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1659073531627655},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.08640298247337341},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2019.1572763","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1572763","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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321106","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W164417758","https://openalex.org/W1489433677","https://openalex.org/W1565187485","https://openalex.org/W1570622790","https://openalex.org/W1866990630","https://openalex.org/W1994038584","https://openalex.org/W2000239378","https://openalex.org/W2005532661","https://openalex.org/W2011287710","https://openalex.org/W2044873480","https://openalex.org/W2046501431","https://openalex.org/W2054957279","https://openalex.org/W2063075538","https://openalex.org/W2067641326","https://openalex.org/W2073864600","https://openalex.org/W2074124346","https://openalex.org/W2085677530","https://openalex.org/W2138378948","https://openalex.org/W2161364915","https://openalex.org/W2323540307","https://openalex.org/W2325725546","https://openalex.org/W2579651919","https://openalex.org/W2591838865","https://openalex.org/W2797508167","https://openalex.org/W3147894994","https://openalex.org/W4249750499"],"related_works":["https://openalex.org/W1973600295","https://openalex.org/W2033333781","https://openalex.org/W1599512561","https://openalex.org/W2057359786","https://openalex.org/W2187751129","https://openalex.org/W4310633471","https://openalex.org/W44636601","https://openalex.org/W1986699031","https://openalex.org/W2006328505","https://openalex.org/W2266771332"],"abstract_inverted_index":{"We":[0],"study":[1],"a":[2],"class":[3],"of":[4,30,46,68,84],"frailty":[5,58,72],"Cox":[6,61,73,79],"models":[7],"with":[8],"measurement":[9,59,77],"error":[10,60,78],"in":[11,38,82],"covariates":[12],"for":[13],"right":[14],"censored":[15],"clustered":[16],"data.":[17],"Based":[18],"on":[19],"the":[20,31,39,44,51],"corrected":[21],"profile":[22],"likelihood":[23],"and":[24,36,49,67,76,86],"Bayes":[25],"estimation,":[26],"we":[27],"construct":[28],"estimators":[29],"regression":[32],"coefficients,":[33],"baseline":[34],"hazard":[35],"frailties":[37],"models,":[40],"which":[41],"can":[42],"reduce":[43],"dimension":[45],"estimated":[47],"parameters":[48],"make":[50],"computation":[52],"feasible.":[53],"Numerical":[54],"results":[55],"show":[56],"that":[57],"model":[62,74,80],"(FMCM)":[63],"is":[64],"more":[65],"competitive":[66],"good":[69],"adaptivity":[70],"than":[71],"(FCM)":[75],"(MCM)":[81],"terms":[83],"bias":[85],"mean":[87],"square":[88],"error.":[89]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}