Selecting the Best Arm in One-Shot Multi-Arm RCTs: The Asymptotic Minimax-Regret Decision Framework for the Best-Population Selection Problem
一次性多臂随机试验的最佳方案选择:渐近极小极大遗憾决策框架
We develop a frequentist decision-theoretic framework for selecting the best arm in one-shot, multi-arm randomized controlled trials (RCTs). Our approach characterizes the minimax-regret (MMR) optimal decision rule for any multivariate location family reward distribution with full support. We show that the MMR rule is deterministic, unique, and computationally tractable. We then specialize to the case of multivariate normal (MVN) rewards with an arbitrary covariance matrix,…