Subset selection for simulations accounting for input uncertainty

We study a subset selection procedure - one of the well-known statistical methods of ranking and selection for stochastic simulations - in the presence of input parameter uncertainty; i.e., the parameters of the input distributions are unknown and there is only a limited amount of input data available for input parameter estimation. The goal is to present a new decision rule which identifies subsets of stochastic system designs including the best (i.e., the design with the largest or smallest expected performance measure) with a probability that exceeds some user-specified value. At WSC 2013, we studied this problem by restricting focus to the method of asymptotic normality approximation to represent input parameter uncertainty. Motivated by the limitations of the asymptotic normality approximation for simulations of complex systems with large numbers of input parameters, we revisit this problem with the simulation replication algorithm as an alternative method to capture input parameter uncertainty.