Ranking and selection techniques with overlapping variance estimators

Some ranking and selection (R&S) procedures for steady-state simulation require an estimate of the asymptotic variance parameter of each system to guarantee a certain probability of correct selection. We show that the performance of such R&S procedures depends on the quality of the variance estimates that are used. In this paper, we study the performance of R&S procedures with two new variance estimators --- overlapping area and overlapping Cramer-von Mises estimators --- which show better long-run performance than other estimators previously used in R&S problems.