We discuss in this paper the strong convergence for weighted sums of negatively orthant dependent (NOD) random variables by generalized Gaussian techniques. As a corollary, a Cesaro law of large numbers of i.i.d. random variables is extended in NOD setting by generalized Gaussian techniques.

In this paper we study the almost universal convergence of weighted sums for sequence {x ,n } of negatively dependent (ND) uniformly bounded random variables, where a, k21 is an may of nonnegative real numbers such that 0(k ) for every ?> 0 and E|x | F | =0 , F = ?(X ,…, X ) for every n>l.

we discuss in this paper the strong convergence for weighted sums of negatively orthant dependent (nod) random variables by generalized gaussian techniques. as a corollary, a cesaro law of large numbers of i.i.d. random variables is extended in nod setting by generalized gaussian techniques.

The aim of this paper is to investigate results on almost sure convergence weighted sums coordinatewise pairwise negatively quadrant dependent random variables taking values in Hilbert spaces. As an application, the degenerate von Mises-statistics investigated.
 Keywords: Negative dependence, spaces, Weighted sums, Strong laws large numbers. 

we propose a wavelet based stochastic regression function estimator for the estimation of the regression function for a sequence of pairwise negative quadrant dependent random variables with a common one-dimensional probability density function. some asymptotic properties of the proposed estimator are investigated. it is found that the estimators have similar properties to their counterparts st...