标题： 顺序抽样数据中的假设检验：AdapRT在独立同分布抽样之外最大化功效 显示英文标题

标题： Hypothesis Testing in Sequentially Sampled Data: AdapRT to Maximize Power Beyond iid Sampling

摘要： 检验感兴趣的变量是否影响结果是统计学中最基本的问题之一，通常是主要的科学问题。 为了解决这个问题，条件随机化检验（CRT）被广泛用于测试感兴趣的变量（X）与结果（Y）之间的独立性，同时保持其他变量（Z）固定。 CRT 使用基于随机化或设计的推断方法，仅依赖于（X,Z）的独立同分布抽样，以任何检验统计量构造精确的小样本p值。 我们提出了一种新方法，自适应随机化检验（ART），在允许数据自适应抽样的情况下解决独立性问题。 我们首先在一个称为正态均值模型的多臂老虎机问题中展示ART。 在这一设定下，我们理论上分析了独立同分布抽样过程和自适应抽样过程的效力，并通过实证发现，ART可以始终优于CRT，该CRT以相等的概率独立抽取所有臂。 我们还意外地发现，当信号相对较强时，ART甚至比使用oracle独立同分布抽样过程的CRT更有效。 我们认为所提出的自适应过程成功是因为它能够处理那些由于随机原因最初看起来像“虚假”信号的臂，并将它们稳定到更接近“零”信号的状态。 我们还将ART应用于一种流行的因子调查设计设置，即联合分析。 通过模拟和一项关于性别歧视在政治候选人评估中的作用的最新应用，我们发现了类似的结果。 显示英文摘要

摘要： Testing whether a variable of interest affects the outcome is one of the most fundamental problem in statistics and is often the main scientific question of interest. To tackle this problem, the conditional randomization test (CRT) is widely used to test the independence of variable(s) of interest (X) with an outcome (Y) holding other variable(s) (Z) fixed. The CRT uses randomization or design-based inference that relies solely on the iid sampling of (X,Z) to produce exact finite-sample p-values that are constructed using any test statistic. We propose a new method, the adaptive randomization test (ART), that tackles the independence problem while allowing the data to be adaptively sampled. We first showcase the ART in a particular multi-arm bandit problem known as the normal-mean model. Under this setting, we theoretically characterize the powers of both the iid sampling procedure and the adaptive sampling procedure and empirically find that the ART can uniformly outperform the CRT that pulls all arms independently with equal probability. We also surprisingly find that the ART can be more powerful than even the CRT that uses an oracle iid sampling procedure when the signal is relatively strong. We believe that the proposed adaptive procedure is successful because it takes arms that may initially look like "fake" signals due to random chance and stabilizes them closer to "null" signals. We additionally showcase the ART to a popular factorial survey design setting known as conjoint analysis. We find similar results through simulations and a recent application concerning the role of gender discrimination in political candidate evaluation.