Asymptotics for the sum of three state Markov dependent random variables

Central and Local Limit Theories in Markov Dependent Random Variables

Estimation of the Survival Function for Negatively Dependent Random Variables

Let be a stationary sequence of pair wise negative quadrant dependent random variables with survival function {,1}nXn?F(x)=P[X>x]. The empirical survival function ()nFx based on 12,,...,nXXX is proposed as an estimator for ()nFx. Strong consistency and point wise as well as uniform of ()nFx are discussed

ON THE LAWS OF LARGE NUMBERS FOR DEPENDENT RANDOM VARIABLES

In this paper, we extend and generalize some recent results on the strong laws of large numbers (SLLN) for pairwise independent random variables [3]. No assumption is made concerning the existence of independence among the random variables (henceforth r.v.’s). Also Chandra’s result on Cesàro uniformly integrable r.v.’s is extended.

On the Complete Convergence ofWeighted Sums for Dependent Random Variables

We study the limiting behavior of weighted sums for negatively associated (NA) random variables. We extend results in Wu (1999) and a theorem in Chow and Lai (1973) for NA random variables.

Sum of arbitrarily dependent random variables

In many classic problems of asymptotic analysis, it appears that the scaled average of a sequence of F -distributed random variables converges to G-distributed limit in some sense of convergence. In this paper, we look at the classic convergence problems from a novel perspective: we aim to characterize all possible limits of the sum of a sequence of random variables under different choices of d...