A study on complete convergence for arrays of rowwise negatively associated random variables

In many probability models, the assumption that random variables under consideration are independent is not plausible. Some random variables are often related to other random variables so an assumption of negative association or dependence is more appropriate than an assumption of mutual independence. In this thesis, complete convergence result for arrays of rowwise negatively associated random variables is obtained, which extends and generalizes the results of Hu, Szynal, and Volodin (1998), Hu, Ordonez Cabrera, Sung, and Volodin (2003), and Sung, Hu, and Volodin (2005). As applications, some well-known results on independent random variables can be easily extended to the case of negatively associated random variables.