Efficient Input Uncertainty Quantification for Regenerative Simulation

Abstract

The initial bias in steady-state simulation can be characterized as the bias of a ratio estimator if the simulation model has a regenerative structure. This work tackles input uncertainty quantification for a regenerative simulation model when its input distributions are estimated from finite data. Our aim is to construct a bootstrap-based confidence interval (CI) for the true simulation output mean performance that provides a correct coverage with significantly less computational cost than the traditional methods. Exploiting the regenerative structure, we propose a $k$-nearest neighbor ($k$NN) ratio estimator for the steady-state performance measure at each set of bootstrapped input models and construct a bootstrap CI from the computed estimators. Asymptotically optimal choices for $k$ and bootstrap sample size are discussed. We further improve the CI by combining the $k$NN and likelihood ratio methods. We empirically compare the efficiency of the proposed estimators with the standard estimator using queueing examples.

Citation

@inproceedings{he2023efficient,
  title={Efficient input uncertainty quantification for regenerative simulation},
  author={He, Linyun and Song, Eunhye and Feng, Ben},
  booktitle={2023 Winter Simulation Conference (WSC)},
  pages={385--396},
  year={2023},
  organization={IEEE}
}