# Optimization-based Quantification of Simulation Input Uncertainty via Empirical Likelihood

arXiv: https://arxiv.org/abs/1707.05917 · HTML: https://mlsanity.com/paper/1707.05917/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Estimation Beyond Data Reweighting: Kernel Method of Moments](https://mlsanity.com/paper/2305.10898/) (2023)
- [Minimax Optimal Estimation of Stability Under Distribution Shift](https://mlsanity.com/paper/2212.06338/) (2022)

## Abstract (arXiv)

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.