Optimal bandwidth selection for recursive Gumbel kernel density estimators
Abstract: In this paper, we propose a data driven bandwidth selection of the recursive Gumbel kernel estimators of a probability density function based on a stochastic approximation algorithm. The choice of the bandwidth selection approaches is investigated by a second generation plug-in method. Convergence properties of the proposed recursive Gumbel kernel estimators are established. The uniform strong consistency of the proposed recursive Gumbel kernel estimators is derived. The new recursive Gumbel kernel estimators are compared to the non-recursive Gumbel kernel estimator and the performance of the two estimators are illustrated via simulations as well as a real application.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Optimal bandwidth selection for recursive Gumbel kernel density estimators ; volume:7 ; number:1 ; year:2019 ; pages:375-393 ; extent:19
Dependence modeling ; 7, Heft 1 (2019), 375-393 (gesamt 19)
- Creator
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Slaoui, Yousri
- DOI
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10.1515/demo-2019-0020
- URN
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urn:nbn:de:101:1-2411181555236.664460161901
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:22 AM CEST
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Associated
- Slaoui, Yousri
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