We review static and dynamic optimization formulations for simulation allocation and
selection procedures and revisit several sampling approaches under a single umbrella. We …

Monte Carlo simulation is a commonly used tool for evaluating the performance of complex
stochastic systems. In practice, simulation can be expensive, especially when comparing a …

We propose a dynamic sampling allocation and selection paradigm for finding the
alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies …

We study a ranking and selection problem with exponential sampling distributions. Under a
Bayesian framework, we derive the posterior distribution of the performance parameter, and …

A value-at-risk, or quantile, is widely used as an appropriate investment selection measure
for risk-conscious decision makers. We present two quantile-based sequential procedures …

Ever since the conception of the statistical ranking-and-selection (R8S) problem, a
predominant approach has been the indifference-zone (IZ) formulation. Under the IZ …

Given a finite number of stochastic systems, the goal of our problem is to dynamically
allocate a finite sampling budget to maximize the probability of selecting the “best” system …

We investigate statistical uncertainty quantification for reinforcement learning (RL) and its
implications in exploration policy. Despite ever-growing literature on RL applications …

This dissertation deals with the various statistical guarantees delivered by ranking-and-
selection (R&S) procedures: a class of methods designed for the problem of selecting the …

We consider the problem of portfolio selection with risk factors, where the goal is to select
the portfolio position that minimizes the value at risk (VaR) of the expected portfolio loss. The …