论文标题
比套房甜：超级马丁格分层的联合交流测试选举
Sweeter than SUITE: Supermartingale Stratified Union-Intersection Tests of Elections
论文作者
论文摘要
例如，分层抽样可用于限制风险审计（RLA），例如容纳异质的投票设备或法律，要求授权管辖权独立绘制其审计样品。我们结合了套件中的联合进间测试，将RLA的减少减少到测试列表集合的平均值是否为Shangrla的$ \ leq 1/2 $，以及在Alpha中的非负Supermartingale（NNSM）测试以提高分层RLA的效率和灵活性。一种简单的非自适应策略，用于结合层状NNSMS，可降低美国密歇根州卡拉马祖的2018年PILOT混合审计的风险超过一个数量级，从0.037到Suite降至0.003。我们给出了一个简单，计算上的简单，自适应规则，以确定在示例中将审计工作量减少多达74％的下一步采样哪个层。我们还提出了基于NNSM的测试，即使有许多地层，也可以在计算方面进行计算处理，并在加利福尼亚州的58个县进行了模拟审核。
Stratified sampling can be useful in risk-limiting audits (RLAs), for instance, to accommodate heterogeneous voting equipment or laws that mandate jurisdictions draw their audit samples independently. We combine the union-intersection tests in SUITE, the reduction of RLAs to testing whether the means of a collection of lists are all $\leq 1/2$ of SHANGRLA, and the nonnegative supermartingale (NNSM) tests in ALPHA to improve the efficiency and flexibility of stratified RLAs. A simple, non-adaptive strategy for combining stratumwise NNSMs decreases the measured risk in the 2018 pilot hybrid audit in Kalamazoo, Michigan, USA by more than an order of magnitude, from 0.037 for SUITE to 0.003 for our method. We give a simple, computationally inexpensive, adaptive rule for deciding which stratum to sample next that reduces audit workload by as much as 74% in examples. We also present NNSM-based tests that are computationally tractable even when there are many strata, illustrated with a simulated audit stratified across California's 58 counties.
