Let {Xn, n≥1} be a stationary sequence of associated random variables and Un be a U-statistic based on this sample. We establish a central limit theorem for Un when the U-statistic is degenerate or non-degenerate using an orthogonal expansion for the kernel associated with Un. We extend the results to U-statistics of kernels of degree 3 and to V-statistics of arbitrary degree. We also establish a central limit theorem for the two sample U-statistic based on observations of two independent stationary associated sequences
Central limit theorems for integrated squared errors of nonparametric kernel estimators of density a...
In this paper, we establish some new central limit theorems for generalized U-statistics of dependen...
We prove the central limit theorem for U-statistics whose underlying sequence of random variables sa...
Let {Xn, n≥1} be a stationary sequence of associated random variables and Un be a U-statistic ...
Let {Xn;n[greater-or-equal, slanted]0} be a sequence of negatively associated random variables and U...
Let {Xn, n ≥ 1} be a sequence of stationary associated random variables. Let Un be a U-statist...
Let {Xn, n ≥ 1} be a sequence of stationary associated random variables. Let Un be a U-statist...
Let {Xn, n[greater-or-equal, slanted]1} be a sequence of stationary associated random variables. Let...
As an estimator of a real estimable parameter, we consider a linear combination of U-statistics whic...
Asymmetric kernels are quite useful for the estimation of density functions with bounded support. Ga...
As an estimator of an estimable parameter, we consider a linear combination of $ mathrm{U}-statistic...
ABSTRACT. Let (X) be a sequence of m-dependent random variables, not necessarily n equally distribut...
AbstractConsider the Kaplan–Meier estimate of the distribution function for right randomly censored ...
A central limit theorem is proved for a class of U-statistics whose kernel depends on the sample siz...
Let {U-n}, n = 1,2,..., be Hilbert space H-valued U-statistics with kernel Phi(.,.), corresponding t...
Central limit theorems for integrated squared errors of nonparametric kernel estimators of density a...
In this paper, we establish some new central limit theorems for generalized U-statistics of dependen...
We prove the central limit theorem for U-statistics whose underlying sequence of random variables sa...
Let {Xn, n≥1} be a stationary sequence of associated random variables and Un be a U-statistic ...
Let {Xn;n[greater-or-equal, slanted]0} be a sequence of negatively associated random variables and U...
Let {Xn, n ≥ 1} be a sequence of stationary associated random variables. Let Un be a U-statist...
Let {Xn, n ≥ 1} be a sequence of stationary associated random variables. Let Un be a U-statist...
Let {Xn, n[greater-or-equal, slanted]1} be a sequence of stationary associated random variables. Let...
As an estimator of a real estimable parameter, we consider a linear combination of U-statistics whic...
Asymmetric kernels are quite useful for the estimation of density functions with bounded support. Ga...
As an estimator of an estimable parameter, we consider a linear combination of $ mathrm{U}-statistic...
ABSTRACT. Let (X) be a sequence of m-dependent random variables, not necessarily n equally distribut...
AbstractConsider the Kaplan–Meier estimate of the distribution function for right randomly censored ...
A central limit theorem is proved for a class of U-statistics whose kernel depends on the sample siz...
Let {U-n}, n = 1,2,..., be Hilbert space H-valued U-statistics with kernel Phi(.,.), corresponding t...
Central limit theorems for integrated squared errors of nonparametric kernel estimators of density a...
In this paper, we establish some new central limit theorems for generalized U-statistics of dependen...
We prove the central limit theorem for U-statistics whose underlying sequence of random variables sa...