AbstractIn this paper we derive the asymptotic normality and a Berry–Esseen type bound for the kernel conditional density estimator proposed in Ould-Saïd and Cai (2005) [26] when the censored observations with multivariate covariates form a stationary α-mixing sequence
Kernel type density estimators are studied for random fields. It is proved that the estimators are a...
The main objective of this work is to estimate, semi-parametrically, the mode of a conditional densi...
In this work, we investigate the asymptotic properties of a nonparametric mode of a conditional dens...
AbstractIn this paper we derive the asymptotic normality and a Berry–Esseen type bound for the kerne...
AbstractIn this paper, we discuss the estimation of a density function based on censored data by the...
We discuss the kernel estimation of a density function based on censored data when the survival and ...
In this paper,we study the asymptotic behaviour of the nonparametric local linear estimation of the ...
In this paper, we study the kernel methods for density estimation of stationary samples under genera...
AbstractWe consider the estimation of the multivariate probability density functions of stationary r...
AbstractIn some long term studies, a series of dependent and possibly censored failure times may be ...
In this paper, we study a smooth estimator of the conditional hazard rate function in the censorship...
By applying the empirical likelihood method, we construct a new weighted estimator of the conditiona...
We consider kernel estimation of bivariate hazard, density and conditional covariance rate function ...
In this paper we consider the nonparametric estimation for a density and hazard rate function for ri...
Abstract In many applications, the available data come from a sampling scheme that causes loss of in...
Kernel type density estimators are studied for random fields. It is proved that the estimators are a...
The main objective of this work is to estimate, semi-parametrically, the mode of a conditional densi...
In this work, we investigate the asymptotic properties of a nonparametric mode of a conditional dens...
AbstractIn this paper we derive the asymptotic normality and a Berry–Esseen type bound for the kerne...
AbstractIn this paper, we discuss the estimation of a density function based on censored data by the...
We discuss the kernel estimation of a density function based on censored data when the survival and ...
In this paper,we study the asymptotic behaviour of the nonparametric local linear estimation of the ...
In this paper, we study the kernel methods for density estimation of stationary samples under genera...
AbstractWe consider the estimation of the multivariate probability density functions of stationary r...
AbstractIn some long term studies, a series of dependent and possibly censored failure times may be ...
In this paper, we study a smooth estimator of the conditional hazard rate function in the censorship...
By applying the empirical likelihood method, we construct a new weighted estimator of the conditiona...
We consider kernel estimation of bivariate hazard, density and conditional covariance rate function ...
In this paper we consider the nonparametric estimation for a density and hazard rate function for ri...
Abstract In many applications, the available data come from a sampling scheme that causes loss of in...
Kernel type density estimators are studied for random fields. It is proved that the estimators are a...
The main objective of this work is to estimate, semi-parametrically, the mode of a conditional densi...
In this work, we investigate the asymptotic properties of a nonparametric mode of a conditional dens...
DOI
Add to ORKG