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 compute and to analyse, and extends simply to multivariate and other estimation problems
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
It is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily hi...
SUMMARY. Hjort and Glad (1995) present a method for semiparametric density estima-tion. Relative to ...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
We introduce a data-perturbation method for reducing bias of a wide variety of density estimators, i...
We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy...
We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
International audienceThis paper introduces a computationally tractable density estimator that has t...
Hjort and Glad (1995) present a method for semiparametric density estima tion. Relative to the ordin...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
It is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily hi...
SUMMARY. Hjort and Glad (1995) present a method for semiparametric density estima-tion. Relative to ...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
We introduce a data-perturbation method for reducing bias of a wide variety of density estimators, i...
We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy...
We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
This paper introduces a computationally tractable density estimator that has the same asymptotic var...
International audienceThis paper introduces a computationally tractable density estimator that has t...
Hjort and Glad (1995) present a method for semiparametric density estima tion. Relative to the ordin...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
It is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily hi...
SUMMARY. Hjort and Glad (1995) present a method for semiparametric density estima-tion. Relative to ...
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