Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Schulmann, Viktor | - |
dc.date.accessioned | 2021-06-07T05:57:17Z | - |
dc.date.available | 2021-06-07T05:57:17Z | - |
dc.date.issued | 2021-03-01 | - |
dc.identifier.uri | http://hdl.handle.net/2003/40240 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-22113 | - |
dc.description.abstract | Let X=(Xt)t≥0 be a known process and T an unknown random time independent of X. Our goal is to derive the distribution of T based on an iid sample of XT. Belomestny and Schoenmakers (Stoch Process Appl 126(7):2092–2122, 2015) propose a solution based the Mellin transform in case where X is a Brownian motion. Applying their technique we construct a non-parametric estimator for the density of T for a self-similar one-dimensional process X. We calculate the minimax convergence rate of our estimator in some examples with a particular focus on Bessel processes where we also show asymptotic normality. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | Statistical inference for stochastic processes;24 | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Estimation of stopping times | en |
dc.subject | Multiplicative deconvolution | en |
dc.subject | Mellin transform | en |
dc.subject | Self-similar process | en |
dc.subject | Bessel process | en |
dc.subject.ddc | 510 | - |
dc.title | Estimation of stopping times for stopped self-similar random processes | en |
dc.type | Text | de |
dc.type.publicationtype | article | de |
dcterms.accessRights | open access | - |
eldorado.secondarypublication | true | de |
eldorado.secondarypublication.primaryidentifier | https://doi.org/10.1007/s11203-020-09234-0 | de |
eldorado.secondarypublication.primarycitation | Schulmann, V. Estimation of stopping times for stopped self-similar random processes. Stat Inference Stoch Process 24, 477–498 (2021). | de |
Appears in Collections: | Lehrstuhl IV Stochastik und Analysis |
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Schulmann2021_Article_EstimationOfStoppingTimesForSt.pdf | 621.16 kB | Adobe PDF | View/Open |
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