Semiparametric linear regression with censored data and stochastic regressors

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dc.contributor.author Mora, Juan
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2009-04-15T06:44:55Z
dc.date.available 2009-04-15T06:44:55Z
dc.date.issued 1994-09
dc.identifier.uri http://hdl.handle.net/10016/3954
dc.description.abstract We propose three new estimation procedures in the linear regression model with randomly-right censored data when the distribution function of the error term is unspecified, regressors are stochastic and the distribution function of the censoring variable is not necessarily the same for all observations ("unequal censoring"). The proposed procedures are derived combining techniques which produce accurate estimates with "equal censoring" with kernel-conditionalı Kaplan-Meier estimates. The performance of six estimation procedures (the three proposed methods and three alternative ones) is compared by means of some Monte Carlo experiments.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 1994-31-12
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Censoring
dc.subject.other Linear regression
dc.subject.other Kaplan-Meier estimator
dc.subject.other Kernel estimator
dc.title Semiparametric linear regression with censored data and stochastic regressors
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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