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dc.contributor.authorKappus, Johannaen_US
dc.description.abstractFor a Lévy process X having finite variation on compact sets and finite first moments, u (dx) = xv (dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of u and provide rates of convergence under regularity assumptions. Moreover, we consider adaptive estimation via model selection and propose a new strategy for the data driven choice of the smoothing parameter.en_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2012-016en_US
dc.subject.keywordstatistics of stochastic processesen_US
dc.subject.keywordlow frequency observed Lévy processesen_US
dc.subject.keywordnonparametric statisticsen_US
dc.subject.keywordadaptive estimationen_US
dc.subject.keywordmodel selection with unknown varianceen_US
dc.subject.stwStochastischer Prozessen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.titleNonparametric adaptive estimation of linear functionals for low frequency observed Lévy processesen_US
dc.typeWorking Paperen_US
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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