AbstractThe problem of nonparametric estimation of a multivariate density function is addressed. In particular, a general class of estimators with favorable asymptotic performance (bias, variance, rate of convergence) is proposed. The proposed estimators are characterized by the flatness near the origin of the Fourier transform of the kernel and are actually shown to be exactlyN-consistent provided the density is sufficiently smooth
AbstractRates of convergence of Mean Squared Error of convolution type estimators of regression func...
AbstractLet X be an Rd-valued random variable with unknown density f. Let X1, …, Xn be i.i.d. random...
We present a novel nonparametric density estimator and a new data-driven bandwidth selection method ...
AbstractLet X be a unit vector random variable taking values on a k-dimensional sphere Ω with probab...
The aim of this thesis is to provide two extensions to the theory of nonparametric kernel density e...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...
AbstractLet X1,…,Xn be n consecutive observations of a linear process X1=μ+∑r=0∞ArZt−r, where μ is a...
Nonparametric kernel estimation of density is widely used, how-ever, many of the pointwise and globa...
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
AbstractMultivariate kernel density estimators are known to systematically deviate from the true val...
We propose a new nonparametric estimator for the density function of multivariate bounded data. As f...
In this paper we propose a new nonparametric kernel based estimator for a density function $f$ which...
Estimators for derivatives associated with a density function can be useful in identifying its modes...
This article considers smooth density estimation based on length biased data that involves a random ...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
AbstractRates of convergence of Mean Squared Error of convolution type estimators of regression func...
AbstractLet X be an Rd-valued random variable with unknown density f. Let X1, …, Xn be i.i.d. random...
We present a novel nonparametric density estimator and a new data-driven bandwidth selection method ...
AbstractLet X be a unit vector random variable taking values on a k-dimensional sphere Ω with probab...
The aim of this thesis is to provide two extensions to the theory of nonparametric kernel density e...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...
AbstractLet X1,…,Xn be n consecutive observations of a linear process X1=μ+∑r=0∞ArZt−r, where μ is a...
Nonparametric kernel estimation of density is widely used, how-ever, many of the pointwise and globa...
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
AbstractMultivariate kernel density estimators are known to systematically deviate from the true val...
We propose a new nonparametric estimator for the density function of multivariate bounded data. As f...
In this paper we propose a new nonparametric kernel based estimator for a density function $f$ which...
Estimators for derivatives associated with a density function can be useful in identifying its modes...
This article considers smooth density estimation based on length biased data that involves a random ...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
AbstractRates of convergence of Mean Squared Error of convolution type estimators of regression func...
AbstractLet X be an Rd-valued random variable with unknown density f. Let X1, …, Xn be i.i.d. random...
We present a novel nonparametric density estimator and a new data-driven bandwidth selection method ...