{"id":"https://openalex.org/W2531579294","doi":"https://doi.org/10.1080/03610918.2016.1242731","title":"SIMEX method for censored quantile regression with measurement error","display_name":"SIMEX method for censored quantile regression with measurement error","publication_year":2016,"publication_date":"2016-10-14","ids":{"openalex":"https://openalex.org/W2531579294","doi":"https://doi.org/10.1080/03610918.2016.1242731","mag":"2531579294"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2016.1242731","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2016.1242731","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/A5039817255","display_name":"Guangcai Mao","orcid":"https://orcid.org/0000-0002-4049-1065"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangcai Mao","raw_affiliation_strings":["School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101455288","display_name":"Wei Yi","orcid":"https://orcid.org/0000-0002-3742-5358"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Wei","raw_affiliation_strings":["School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100416810","display_name":"Yanyan Liu","orcid":"https://orcid.org/0000-0002-1405-7345"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanyan Liu","raw_affiliation_strings":["School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100416810"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":0.3498,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69356275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"46","issue":"10","first_page":"7552","last_page":"7560"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9718000292778015,"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":0.9573000073432922,"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/covariate","display_name":"Covariate","score":0.8679836988449097},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.8119570016860962},{"id":"https://openalex.org/keywords/censoring","display_name":"Censoring (clinical trials)","score":0.7739148139953613},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6711646318435669},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.6168208718299866},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5376485586166382},{"id":"https://openalex.org/keywords/observational-error","display_name":"Observational error","score":0.531091570854187},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5036429762840271},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4889043867588043},{"id":"https://openalex.org/keywords/regression-dilution","display_name":"Regression dilution","score":0.4882188141345978},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.4802875220775604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45427072048187256},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.4434431791305542},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39864739775657654},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.14134320616722107}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8679836988449097},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.8119570016860962},{"id":"https://openalex.org/C137668524","wikidata":"https://www.wikidata.org/wiki/Q189813","display_name":"Censoring (clinical trials)","level":2,"score":0.7739148139953613},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6711646318435669},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.6168208718299866},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5376485586166382},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.531091570854187},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5036429762840271},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4889043867588043},{"id":"https://openalex.org/C185265303","wikidata":"https://www.wikidata.org/wiki/Q7309537","display_name":"Regression dilution","level":4,"score":0.4882188141345978},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.4802875220775604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45427072048187256},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.4434431791305542},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39864739775657654},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.14134320616722107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2016.1242731","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2016.1242731","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":0.5799999833106995,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"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":24,"referenced_works":["https://openalex.org/W1512882103","https://openalex.org/W1580788756","https://openalex.org/W1893083699","https://openalex.org/W1975286873","https://openalex.org/W1985732394","https://openalex.org/W2001232785","https://openalex.org/W2007850519","https://openalex.org/W2009259977","https://openalex.org/W2033023044","https://openalex.org/W2036038497","https://openalex.org/W2059053591","https://openalex.org/W2059331042","https://openalex.org/W2061250208","https://openalex.org/W2083972068","https://openalex.org/W2096945205","https://openalex.org/W2103186958","https://openalex.org/W2107368116","https://openalex.org/W2169649310","https://openalex.org/W2171720857","https://openalex.org/W2564630848","https://openalex.org/W3147894994","https://openalex.org/W4241653265","https://openalex.org/W4247545505","https://openalex.org/W4285719527"],"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":{"Censored":[0],"quantile":[1,65],"regression":[2,66],"serves":[3],"as":[4],"an":[5,48],"important":[6],"supplement":[7],"to":[8,19,22,28,33,51,62,84],"the":[9,53,59,63,86,89],"Cox":[10],"proportional":[11],"hazards":[12],"model":[13],"in":[14],"survival":[15],"analysis.":[16],"In":[17],"addition":[18],"being":[20],"exposed":[21],"censoring,":[23],"some":[24],"covariates":[25],"may":[26],"subject":[27],"measurement":[29,54,69],"error.":[30,70],"This":[31],"leads":[32],"substantially":[34],"biased":[35],"estimate":[36],"without":[37],"taking":[38],"this":[39],"error":[40,55],"into":[41],"account.":[42],"The":[43,71],"SIMulation-EXtrapolation":[44],"(SIMEX)":[45],"method":[46],"is":[47,73,82],"effective":[49],"tool":[50],"handle":[52],"issue.":[56],"We":[57],"extend":[58],"SIMEX":[60],"approach":[61],"censored":[64],"with":[67],"covariate":[68],"algorithm":[72],"assessed":[74],"via":[75],"extensive":[76],"simulations.":[77],"A":[78],"lung":[79],"cancer":[80],"study":[81],"analyzed":[83],"verify":[85],"validation":[87],"of":[88],"proposed":[90],"method.":[91]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"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"}
