Local superefficiency of data-driven projection density estimators in continuous time
We construct a data-driven projection density estimator for continuous time processes. This estimator reaches superoptimal rates over a class F0 of densities that is dense in the family of all possible densities, and a «reasonable» rate elsewhere. The class F0 may be chosen previously by the analyst...
| Authors: | , |
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| Format: | article |
| Publication Date: | 2004 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3745 |
| Online Access: | https://hdl.handle.net/2099/3745 |
| Access Level: | Open access |
| Keyword: | Inference Inferència Processos estocàstics Classificació AMS::62 Statistics::62G Nonparametric inference Classificació AMS::62 Statistics::62M Inference from stochastic processes |
| Summary: | We construct a data-driven projection density estimator for continuous time processes. This estimator reaches superoptimal rates over a class F0 of densities that is dense in the family of all possible densities, and a «reasonable» rate elsewhere. The class F0 may be chosen previously by the analyst. Results apply to Rd-valued processes and to N-valued processes. In the particular case where squareintegrable local time does exist, it is shown that our estimator is strictly better than the local time estimator over F0. |
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