论文标题
负面依赖框架评估不同形式的扰动
A negative dependence framework to assess different forms of scrambling
论文作者
论文摘要
我们使用依赖的框架来评估faure和Halton序列随机争夺的好处。我们试图回答以下问题：当确定性序列具有针对小样本量的已知缺陷时，我们是否应该通过应用随机扰动来解决这些缺陷，还是应该找到“良好”的确定性拼凑而产生的序列，然后使用较低的计算机密集型随机化方法随机分组，例如数字移位？在后一种情况下，我们如何选择确定性的争夺，以及如何评估它是否好？
We use the framework of dependence to assess the benefits of scrambling randomly versus deterministically for Faure and Halton sequences. We attempt to answer the following questions: when a deterministic sequence has known defects for small sample sizes, should we address these defects by applying random scrambling or should we find a "good" deterministic scrambling yielding a sequence that can then be randomized using a less computer-intensive randomization method such as a digital shift? And in the latter case, how do we choose a deterministic scrambling and how do we assess whether it is good or not?
