A0166
Title: Estimation of expectations in two-level nested simulation experiments
Authors: David Fernando Munoz - Instituto Tecnologico Autonomo de Mexico (Mexico) [presenting]
Abstract: Input parameters of a simulation experiment are usually estimated from real-data observations, and parameter uncertainty can be significant when little data is available. In this case, Bayesian statistics can be used to incorporate this uncertainty in the output analysis of simulation experiments via the use of a posterior distribution. A methodology currently proposed for the analysis of simulation experiments under parameter uncertainty is a two-level nested simulation method. In the outer level, we simulate $n$ observations for the parameters from a posterior distribution, while in the inner level, we simulate $m$ observations for the response variable with the parameter fixed at the value generated in the outer level. In this paper, we focus on the output analysis of two-level simulation experiments, for the case where the observations of the inner level are independent, showing how the variance of a simulated observation can be decomposed into parametric and stochastic variance components. We derive a Central Limit Theorem (CLT) for both the estimator of the point forecast and the estimators of the variance components. Our CLTs allow us to compute asymptotic confidence intervals for each estimator. Our theoretical results are validated through experiments with a forecasting model for sporadic demand.
