Add to ORKGThe problem of the probability density estimation by using n size sample of stationary process is considered. We investigate conditions which the coefficients in approximation of Mean Integrated Squared Error should satisfy to receive the consistent estimator. The clear approximation formula of optimal bandwidth for dependent data is given
An approximate necessary condition for the optimal bandwidth choice is derived. This condition is us...
In this investigation, the problem of estimating the probability density function of a function of m...
In the current investigation, the problem of estimating the probability density of a function of m i...
The problem of the probability density estimation by using n size sample of stationary process is c...
We derive optimal bandwidths for kernel density estimators of functions of observations proposed in ...
In this paper, we consider the integrated square error where f is the common density function of the...
The performance of kernel density estimation, in terms of mean integrated squared error, is investig...
In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing param...
AbstractFor the purpose of comparing different nonparametric density estimators, Wegman (J. Statist....
We derive optimal bandwidths for kernel density estimators of functions of observations proposed in ...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...
There are various methods for estimating a density. A group of methods which estimate the density as...
A class of data-based bandwidth selection procedures for kernel density estimation is investigated. ...
Kernel density estimates are a robust way to reconstruct a continuous distribution from a discrete p...
Kernel density estimation, mean integrated squared error, optimal kernel, regular variation,
An approximate necessary condition for the optimal bandwidth choice is derived. This condition is us...
In this investigation, the problem of estimating the probability density function of a function of m...
In the current investigation, the problem of estimating the probability density of a function of m i...
The problem of the probability density estimation by using n size sample of stationary process is c...
We derive optimal bandwidths for kernel density estimators of functions of observations proposed in ...
In this paper, we consider the integrated square error where f is the common density function of the...
The performance of kernel density estimation, in terms of mean integrated squared error, is investig...
In kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing param...
AbstractFor the purpose of comparing different nonparametric density estimators, Wegman (J. Statist....
We derive optimal bandwidths for kernel density estimators of functions of observations proposed in ...
The unknown error density of a nonparametric regression model is approximated by a mixture of Gaussi...
There are various methods for estimating a density. A group of methods which estimate the density as...
A class of data-based bandwidth selection procedures for kernel density estimation is investigated. ...
Kernel density estimates are a robust way to reconstruct a continuous distribution from a discrete p...
Kernel density estimation, mean integrated squared error, optimal kernel, regular variation,
An approximate necessary condition for the optimal bandwidth choice is derived. This condition is us...
In this investigation, the problem of estimating the probability density function of a function of m...
In the current investigation, the problem of estimating the probability density of a function of m i...