The nonparametric location-scale mixture cure model

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dc.contributor.authorChown, Justin
dc.contributor.authorHeuchenne, Cédric
dc.contributor.authorVan Keilegom, Ingrid
dc.date.accessioned2018-03-27T14:52:08Z
dc.date.available2018-03-27T14:52:08Z
dc.date.issued2018
dc.description.abstractWe propose completely nonparametric methodology to investigate location-scale modelling of two-component mixture cure models, where the responses of interest are only indirectly observable due to the presence of censoring and the presence of so-called long-term survivors that are always censored. We use covariate-localized nonparametric estimators, which depend on a bandwidth sequence, to propose an estimator of the error distribution function that has not been considered before in the literature. When this bandwidth belongs to a certain range of undersmoothing band-widths, the asymptotic distribution of the proposed estimator of the error distribution function does not depend on this bandwidth, and this estimator is shown to be root-n consistent. This suggests that a computationally costly bandwidth selection procedure is unnecessary to obtain an effective estimator of the error distribution, and that a simpler rule-of-thumb approach can be used instead. A simulation study investigates the finite sample properties of our approach, and the methodology is illustrated using data obtained to study the behavior of distant metastasis in lymph-node-negative breast cancer patients.en
dc.identifier.urihttp://hdl.handle.net/2003/36818
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-18819
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;7/2018
dc.subjectcensored dataen
dc.subjectnonparametric regressionen
dc.subjecterror distribution functionen
dc.subjectcure modelen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleThe nonparametric location-scale mixture cure modelen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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