In this paper, we aim at highlighting the in uence of the density pole on the performances of its gamma-kernel estimator. To do this, we performed a comparative study for the performances of the gamma-kernel estimators with those provided by other bias effect correction techniques at the bounds, using the simulation technique. In conclusion, the results obtained conrm those provided in the literature and show that in some cases the normalization of the gamma estimators can considerably improve local and global performances of the gamma-kernel estimators
Hjort and Glad (1995) present a method for semiparametric density estima tion. Relative to the ordin...
The best mean square error that the classical kernel density estimator achieves if the kernel is non...
In this paper we consider the nonparametric estimation for a density and hazard rate function for ri...
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive...
The nonparametric estimation for the density and hazard rate functions for right-censored data using...
We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as th...
In this thesis we investigate the convergence rate of gamma kernel estimators in recursive density e...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
The performance of kernel density estimation, in terms of mean integrated squared error, is investig...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
Kernel density estimation is a widely used method for estimating a distribution based on a sample of...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
Two methods are suggested for removing the problem of negativity of high-order kernel density estima...
AbstractTwo methods are suggested for removing the problem of negativity of high-order kernel densit...
Hjort and Glad (1995) present a method for semiparametric density estima tion. Relative to the ordin...
The best mean square error that the classical kernel density estimator achieves if the kernel is non...
In this paper we consider the nonparametric estimation for a density and hazard rate function for ri...
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive...
The nonparametric estimation for the density and hazard rate functions for right-censored data using...
We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as th...
In this thesis we investigate the convergence rate of gamma kernel estimators in recursive density e...
The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is ...
The performance of kernel density estimation, in terms of mean integrated squared error, is investig...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
In this thesis, we study some boundary correction methods for kernel estimators of the density funct...
Kernel density estimation is a widely used method for estimating a distribution based on a sample of...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
Two methods are suggested for removing the problem of negativity of high-order kernel density estima...
AbstractTwo methods are suggested for removing the problem of negativity of high-order kernel densit...
Hjort and Glad (1995) present a method for semiparametric density estima tion. Relative to the ordin...
The best mean square error that the classical kernel density estimator achieves if the kernel is non...
In this paper we consider the nonparametric estimation for a density and hazard rate function for ri...