International audienceThis paper introduces a computationally tractable density estimator that has the same asymptotic variance as the classical Nadaraya-Watson density estimator but whose asymptotic bias is zero. We achieve this result using a two stage estimator that applies a multiplicative bias correction to an oversmooth pilot estimator. Simulations show that our asymptotic results are available for samples as low as n = 50, where we see an improvement of as much as 20% over the traditionnal estimator. Mathematics Subject Classification. 62G07, 62G20
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...
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...
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...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We present a kernel estimator for the density of a variable when sampling probabilities depend on th...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
This article considers smooth density estimation based on length biased data that involves a random ...
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...
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...
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...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We present a kernel estimator for the density of a variable when sampling probabilities depend on th...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
This article considers smooth density estimation based on length biased data that involves a random ...
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
With the aim of mitigating the possible problem of negativity in the estimation of the conditional d...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...