Add to ORKGRate of convergence to normality for the density estimators of Kernel type is obtained when the observations are from a stationary linear processes. At first, the case of estimating the density at a fixed point is considered and latter on, it is extended for estimating joint density. Also the problem of estimating the density at several points is considered
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
Let X1,...,Xn be n consecutive observations of a linear process , where [mu] is a constant and {Zt} ...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
AbstractLet X1,…,Xn be n consecutive observations of a linear process X1=μ+∑r=0∞ArZt−r, where μ is a...
Nonlinear process, kernel type density estimators, bilinear process, central limit theorem, almost s...
AbstractIn this paper moving-average processes with no parametric assumption on the error distributi...
A method is proposed for creating a smooth kernel density estimate from a sample of binned data. Sim...
International audienceSome convergence results on the kernel density estimator are proven for a clas...
A method is proposed for creating a smooth kernel density estimate from a sample of binned data. Sim...
This paper studies the problem of estimating the density of U when only independent copies of X = U ...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
Let X1,...,Xn be n consecutive observations of a linear process , where [mu] is a constant and {Zt} ...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
AbstractLet X1,…,Xn be n consecutive observations of a linear process X1=μ+∑r=0∞ArZt−r, where μ is a...
Nonlinear process, kernel type density estimators, bilinear process, central limit theorem, almost s...
AbstractIn this paper moving-average processes with no parametric assumption on the error distributi...
A method is proposed for creating a smooth kernel density estimate from a sample of binned data. Sim...
International audienceSome convergence results on the kernel density estimator are proven for a clas...
A method is proposed for creating a smooth kernel density estimate from a sample of binned data. Sim...
This paper studies the problem of estimating the density of U when only independent copies of X = U ...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric ...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...