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.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
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)
- Urheber
-
Slaoui, Yousri
- DOI
-
10.1515/demo-2019-0020
- URN
-
urn:nbn:de:101:1-2411181555236.664460161901
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:22 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Slaoui, Yousri
Ähnliche Objekte (12)
Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions
Bandwidth Selection in Nonparametric Kernel Estimation
Local bandwidth selection in nonparametric kernel regression
Capital allocation for credit portfolios with kernel estimators
Capital allocation for credit portfolios with kernel estimators
Concentration inequalities for Kernel density estimators under uniform mixing
Antithetic and Monte Carlo kernel estimators for partial rankings
Confidence sets in decision problems with kernel density estimators
Adaptive bandwidth kernel density estimation for next-generation sequencing data
Uniform consistency of modified kernel estimators in parametric ARCH-models
Plug-in bandwidth selection for kernel density estimation with discrete data
A Review and Comparison of Bandwidth Selection Methods for Kernel Regression
Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions
Bandwidth Selection in Nonparametric Kernel Estimation
Local bandwidth selection in nonparametric kernel regression
Capital allocation for credit portfolios with kernel estimators
Capital allocation for credit portfolios with kernel estimators
Concentration inequalities for Kernel density estimators under uniform mixing
Antithetic and Monte Carlo kernel estimators for partial rankings
Confidence sets in decision problems with kernel density estimators
Adaptive bandwidth kernel density estimation for next-generation sequencing data
Uniform consistency of modified kernel estimators in parametric ARCH-models
Plug-in bandwidth selection for kernel density estimation with discrete data
A Review and Comparison of Bandwidth Selection Methods for Kernel Regression
Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions
Bandwidth Selection in Nonparametric Kernel Estimation
Local bandwidth selection in nonparametric kernel regression
Capital allocation for credit portfolios with kernel estimators
Capital allocation for credit portfolios with kernel estimators
Concentration inequalities for Kernel density estimators under uniform mixing
Antithetic and Monte Carlo kernel estimators for partial rankings
Confidence sets in decision problems with kernel density estimators
Adaptive bandwidth kernel density estimation for next-generation sequencing data
Uniform consistency of modified kernel estimators in parametric ARCH-models
Plug-in bandwidth selection for kernel density estimation with discrete data