AbstractThere have important applications of density kernel estimation in statistics. In certain conditions, we obtain the convergence rate of multidimensional density kernel estimation by exploiting Bootstrap
We study the weighted bootstrap of the empirical process indexed by a class of functions, when the w...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
We propose a smoothed bootstrap estimator M∗n of the MISE Mn of a kernel density estimator based on ...
We propose a generalized smooth bootstrap scheme for estimating the bias By and mean square error My...
Let f be a multivariate density and fn be a kernel estimate of f drawn from the n-sample X1,...,Xn o...
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density e...
This paper studies the problem of estimating the density of U when only independent copies of X = U ...
AbstractA smooth bootstrap method is used to find an estimator of the mean integrated squared error ...
An approach to bootstrapping kernel spectral density estimates is described which is based on resamp...
A method is proposed for creating a smooth kernel density estimate from a sample of binned data. Sim...
Let X be an -valued random variable with unknown density f. Let X1,...,Xn be i.i.d. random variables...
We study the weighted bootstrap of the empirical process indexed by a class of functions, when the w...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
We propose a smoothed bootstrap estimator M∗n of the MISE Mn of a kernel density estimator based on ...
We propose a generalized smooth bootstrap scheme for estimating the bias By and mean square error My...
Let f be a multivariate density and fn be a kernel estimate of f drawn from the n-sample X1,...,Xn o...
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density e...
This paper studies the problem of estimating the density of U when only independent copies of X = U ...
AbstractA smooth bootstrap method is used to find an estimator of the mean integrated squared error ...
An approach to bootstrapping kernel spectral density estimates is described which is based on resamp...
A method is proposed for creating a smooth kernel density estimate from a sample of binned data. Sim...
Let X be an -valued random variable with unknown density f. Let X1,...,Xn be i.i.d. random variables...
We study the weighted bootstrap of the empirical process indexed by a class of functions, when the w...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
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