A new method for bias reduction in nonparametric density estimation is proposed. The method is a simple, two-stage multiplicative bias correction. Its theoretical properties are investigated, and simulations indicate its practical potential. The method is easy to com-pute and to analyse, and extends simply to multivariate and other estimation problems. Some key words: Kernel smoothing; Multiplicative bias correction; Transformation
The paper presents a multiplicative bias reduction estimator for nonparametric regression. The appro...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
The paper presents a multiplicative bias reduction estimator for nonparametric regression. The appro...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy...
SUMMARY. Hjort and Glad (1995) present a method for semiparametric density estima-tion. Relative to ...
We introduce a data-perturbation method for reducing bias of a wide variety of density estimators, i...
We introduce a multiplicative bias reducing estimator (MBRE) for nonparametric regression. We show t...
29 pagesThe paper presents a multiplicative bias reduction estimator for nonparametric regression. T...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as th...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
The transformation kernel density estimator of Ruppert and Cline (1994) achieves bias of order h4 (a...
The paper presents a multiplicative bias reduction estimator for nonparametric regression. The appro...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
The paper presents a multiplicative bias reduction estimator for nonparametric regression. The appro...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy...
SUMMARY. Hjort and Glad (1995) present a method for semiparametric density estima-tion. Relative to ...
We introduce a data-perturbation method for reducing bias of a wide variety of density estimators, i...
We introduce a multiplicative bias reducing estimator (MBRE) for nonparametric regression. We show t...
29 pagesThe paper presents a multiplicative bias reduction estimator for nonparametric regression. T...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as th...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
The transformation kernel density estimator of Ruppert and Cline (1994) achieves bias of order h4 (a...
The paper presents a multiplicative bias reduction estimator for nonparametric regression. The appro...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
The paper presents a multiplicative bias reduction estimator for nonparametric regression. The appro...
Add to ORKG