4 papers
Studentized Cheap Bootstrap: Achieving Higher-Order Coverage Accuracy with Low Computation
Shengyi He, Henry Lam, Yunhao Yan
The bootstrap is a versatile method for quantifying statistical uncertainty. Among its variants, a popular approach, the studentized bootstrap, provably achieves higher-order cover…
Shape-Constrained Distributional Optimization via Importance-Weighted Sample Average Approximation
Henry Lam, Zhenyuan Liu, Dashi I. Singham
Shape-constrained optimization arises in a wide range of problems including distributionally robust optimization (DRO) that has surging popularity in recent years. In the DRO liter…
Quantifying Distributional Input Uncertainty via Inflated Kolmogorov-Smirnov Confidence Band
Motong Chen, Henry Lam, Zhenyuan Liu
In stochastic simulation, input uncertainty refers to the propagation of the statistical noise in calibrating input models to impact output accuracy, in addition to the Monte Carlo…
Propagation of Input Tail Uncertainty in Rare-Event Estimation: A Light versus Heavy Tail Dichotomy
Zhiyuan Huang, Henry Lam, Zhenyuan Liu
We consider the estimation of small probabilities or other risk quantities associated with rare but catastrophic events. In the model-based literature, much of the focus has been d…