Censored quantile regression processes under dependence and penalization

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dc.contributor.authorDette, Holger
dc.contributor.authorVolgushev, Stanislav
dc.contributor.authorWagener, Jens
dc.date.accessioned2012-08-28T12:43:26Z
dc.date.available2012-08-28T12:43:26Z
dc.date.issued2012-08-28
dc.description.abstractWe consider quantile regression processes from censored data under dependent data structures and derive a uniform Bahadur representation for those processes. We also consider cases where the dimension of the parameter in the quantile regression model is large. It is demonstrated that traditional penalization methods such as the adaptive lasso yield sub-optimal rates if the coe fficients of the quantile regression cross zero. New penalization techniques are introduced which are able to deal with speci c problems of censored data and yield estimates with an optimal rate. In contrast to most of the literature, the asymptotic analysis does not require the assumption of independent observations, but is based on rather weak assumptions, which are satis ed for many kinds of dependent data.en
dc.identifier.urihttp://hdl.handle.net/2003/29598
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-4908
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;34/2012
dc.subjectBahadur representationen
dc.subjectcensored dataen
dc.subjectdependent dataen
dc.subjectquantile regressionen
dc.subjectvariable selectionen
dc.subjectweak convergenceen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleCensored quantile regression processes under dependence and penalizationen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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