The paper presents a multiplicative bias reduction estimator for nonparametric regression. The approach consists to apply a multi-plicative bias correction to an oversmooth pilot estimator. In Burr et al. [2010], this method has been tested to estimate energy spectra. For such data set, it was observed that the method allows to decrease bias with negligible increase in variance. In this paper, we study the asymptotic properties of the resulting estimate and prove that this estimate has zero asymptotic bias and the same asymptotic variance as the local linear estimate. Simulations show that our asymptotic results are available for modest sample sizes. Index terms: Nonparametric regression, bias reduction, local linear estimate
The local polynomial estimator is particularly affected by the curse of dimensionality, which reduce...
This paper proposes a nonparametric bias-reduction regression estimator which can accommodate two em...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
29 pagesInternational audienceThe paper presents a multiplicative bias reduction estimator for nonpa...
The paper presents a multiplicative bias reduction estimator for non-parametric regression. The appr...
We introduce a multiplicative bias reducing estimator (MBRE) for nonparametric regression. We show t...
In this article, we propose a new method of bias reduction in nonparametric regression estimation. T...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
Nonparametric and semiparametric regression models are useful statistical regression models to disco...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
We propose and investigate two new methods for achieving less bias in non- parametric regression. We...
The purpose of this study is to determine the effect of three improvement methods on nonparametric k...
The chapters that constitute my dissertation can be briefly summarized as follows. Chapter 1 studies...
Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996...
Nonparametric methods play a central role in modern empirical work. While they provide inference pro...
The local polynomial estimator is particularly affected by the curse of dimensionality, which reduce...
This paper proposes a nonparametric bias-reduction regression estimator which can accommodate two em...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
29 pagesInternational audienceThe paper presents a multiplicative bias reduction estimator for nonpa...
The paper presents a multiplicative bias reduction estimator for non-parametric regression. The appr...
We introduce a multiplicative bias reducing estimator (MBRE) for nonparametric regression. We show t...
In this article, we propose a new method of bias reduction in nonparametric regression estimation. T...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
Nonparametric and semiparametric regression models are useful statistical regression models to disco...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
We propose and investigate two new methods for achieving less bias in non- parametric regression. We...
The purpose of this study is to determine the effect of three improvement methods on nonparametric k...
The chapters that constitute my dissertation can be briefly summarized as follows. Chapter 1 studies...
Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996...
Nonparametric methods play a central role in modern empirical work. While they provide inference pro...
The local polynomial estimator is particularly affected by the curse of dimensionality, which reduce...
This paper proposes a nonparametric bias-reduction regression estimator which can accommodate two em...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...