Let fXn; n  1g be a strictly stationary sequence of negatively associated ran- dom variables, with common continuous and bounded distribution function F. In this paper, we consider the estimation of the two-dimensional distribution function of (X1;Xk+1) based on Kernel type estimators as well as the estimation of the covariance function of the limit empirical process induced by the sequence fXn; n  1g. Then, we derive uniform strong convergence rates for the Kernel estimator of two-dimensional distribution function of (X1;Xk+1) which were not found already and do not need any conditions on the covariance structure of the variables. Finally, assuming a convenient decrease rate of the covariances Cov(X1;Xn+1); n  1, we introduce uniform strong convergence rate for covariance function of the limit empirical process based on Kernel type estimators.

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