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.
翻译:例如,分层抽样可以用于风险限制审计(RLAs),以容纳不同投票设备或授权管辖机构独立抽取审计样本的法律。我们把SUITE中的工会间抽样测试、减少RLA以测试名单收集手段是否全部为SHANGRALA的0.2美元,以及ALPHA中的非负式超级上层抽样测试(NNSM),以提高分层国家抽样审计的效率和灵活性。一个简单、非适应性的合并分层国家抽样调查的合并战略,降低了2018年在美国密歇根州卡拉马祖的试点混合审计中测得的风险,从0.37美元到0.003美元不等。我们给出了一个简单、计算成本低廉和适应性的规则,用以决定下一个样本的哪一层的抽样降低了审计工作量,例如高达74%。我们还提供了基于国家抽样调查的测试,即使有多个层次,也可计算为可查询的,并用58个州的模拟审计标准加以说明。