Nonparametric inference for discretely sampled Lévy processes

S. Gugushvili

Onderzoeksoutput: Boek/rapportRapportAcademic

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Given a sample from a discretely observed Lévy process $ X = (X_t)_{t \geq 0} $ of the finite jump activity, the problem of nonparametric estimation of the Lévy density \rho corresponding to the process X is studied. An estimator of \rho is proposed that is based on a suitable inversion of the Lévy-Khintchine formula and a plug-in device. The main results of the paper deal with upper risk bounds for estimation of \rho over suitable classes of Lévy triplets. The corresponding lower bounds are also discussed.
Originele taal-2Engels
Plaats van productieEindhoven
Aantal pagina's24
UitgaveRevision 25 May 2011 (v3)
StatusGepubliceerd - 2009

Publicatie series

NaamReport Eurandom
ISSN van geprinte versie1389-2355


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