Optimization-based Quantification of Simulation Input Uncertainty via Empirical Likelihood

TL;DR

This paper introduces an optimization-based method using empirical likelihood to construct accurate confidence intervals for simulation performance measures under input uncertainty, offering computational and finite-sample advantages over traditional methods.

Contribution

It develops a novel empirical likelihood-based framework for quantifying simulation input uncertainty with statistical guarantees and improved efficiency.

Findings

Provides statistically valid confidence intervals for simulation outputs.

Demonstrates computational efficiency over bootstrap and delta methods.

Ensures finite-sample performance with tight statistical guarantees.

Abstract

We study an optimization-based approach to construct statistically accurate confidence intervals for simulation performance measures under nonparametric input uncertainty. This approach computes confidence bounds from simulation runs driven by probability weights defined on the data, which are obtained from solving optimization problems under suitably posited averaged divergence constraints. We illustrate how this approach offers benefits in computational efficiency and finite-sample performance compared to the bootstrap and the delta method. While resembling robust optimization, we explain the procedural design and develop tight statistical guarantees of this approach via a generalization of the empirical likelihood method.