{"data":{"id":"10.6084/m9.figshare.c.2031245.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2031245.v1","prefix":"10.6084","suffix":"m9.figshare.c.2031245.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Yuanshan Wu","affiliation":[],"nameIdentifiers":[]},{"name":"Yanyuan Ma","affiliation":[],"nameIdentifiers":[]},{"name":"Guosheng Yin","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"Medicine"},{"subject":"Biotechnology"},{"subject":"59999 Environmental Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Earth and related environmental sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2016-04-29","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/collections/Smoothed_and_Corrected_Score_Approach_to_Censored_Quantile_Regression_With_Measurement_Errors/2031245/1","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2015-12-05T19:30:37.000Z","registered":"2015-12-05T19:30:38.000Z","published":"2015","updated":"2024-12-12T13:55:11.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clie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