Add to ORKGRate of convergence for density estimators based on Haar series are derived under very mild condition: the unknown density has to be of bounded variation. These estimators are histograms on dyadic intervals.Orthogonal series density estimator Haar series histogram mean integrated absolute error bounded variation
We consider the smoothed histograms by gamma densities for an i.i.d. sample defined on the positive ...
The problem of prior elicitation often arises when one is interested in doing inference in a Bayesia...
Assuming that (Xn)n∈Z is a vector valued time series with a common mar-ginal distribution admitting ...
Abstract: Rate of convergence for density estimators based on Haar series are derived under very mil...
We consider estimation of multivariate densities with histograms which are based on data-dependent p...
There exist many ways to estimate the shape of the underlying density. Generally, we can categorize ...
Kernal density estimators are used for estimation of integrals of various squared derivatives of a p...
We study the Lp-integrated risk of some classical estimators of the density, when the observations a...
The main result of this paper is summarized in Theorem 1, which states that when certain conditions ...
We introduce simple nonparametric density estimators that generalize the classical histogram and fre...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
This paper studies the problem of estimating the density of U when only independent copies of X = U ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Let R(,L(,1))(f(,n),f), R(,K)(f(,n),f) be the risks of the density estimator f(,n) of(\u27 ) the den...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We consider the smoothed histograms by gamma densities for an i.i.d. sample defined on the positive ...
The problem of prior elicitation often arises when one is interested in doing inference in a Bayesia...
Assuming that (Xn)n∈Z is a vector valued time series with a common mar-ginal distribution admitting ...
Abstract: Rate of convergence for density estimators based on Haar series are derived under very mil...
We consider estimation of multivariate densities with histograms which are based on data-dependent p...
There exist many ways to estimate the shape of the underlying density. Generally, we can categorize ...
Kernal density estimators are used for estimation of integrals of various squared derivatives of a p...
We study the Lp-integrated risk of some classical estimators of the density, when the observations a...
The main result of this paper is summarized in Theorem 1, which states that when certain conditions ...
We introduce simple nonparametric density estimators that generalize the classical histogram and fre...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
This paper studies the problem of estimating the density of U when only independent copies of X = U ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Let R(,L(,1))(f(,n),f), R(,K)(f(,n),f) be the risks of the density estimator f(,n) of(\u27 ) the den...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We consider the smoothed histograms by gamma densities for an i.i.d. sample defined on the positive ...
The problem of prior elicitation often arises when one is interested in doing inference in a Bayesia...
Assuming that (Xn)n∈Z is a vector valued time series with a common mar-ginal distribution admitting ...