Constructing Simulation Output Intervals Under Input Uncertainty via Data SectioningPeter W. Glynn and Henry Lam Proceedings of the 2018 Winter Simulation Conference. We study the problem of constructing confidence intervals (CIs) for simulation outputs in the presence of input uncertainty, where the constructed CIs capture both the statistical noises from the simulation replications and the input data. We present a simple technique based on sectioning input data that provides exact asymptotic confidence guarantees. Unlike some existing approaches, our technique bypasses the need to consistently estimate variances that could be computationally demanding. It can be flexibly applied to dependent data and to both parametric and nonparametric input models. |