AI 中文总结
本研究定义三类测试评估数据间距特征，经双峰设置及文献测试用例检验，证实间距可反映数据模态，测试能筛选边缘案例并明确分析失效位置。
AI 中文摘要
数据的间距包含了关于底层模态的信息，一致、相似的间距段对应模态，而模态之间的间距会增大。我们已定义了参数化、基于游程的非参数化以及数据驱动的测试，以评估这些特征——平坦段和峰值，并确定多模态的存在及位置。本报告将在两种情境下检验这些测试：通过改变双峰设置，我们可控制间距的变化，确定测试的分辨率和灵敏度；通过将其应用于其他模态研究文献中存在的大量测试用例，我们检验结果的稳定性和一致性。我们还将评估这些特征的零分布模型的准确性。结果表明，间距确实能反映数据的模态，这些测试确实能筛选边缘案例，以及分析开始失效的位置。
英文摘要
The spacing of data contains information about the underlying modality. Sections of consistent, similar spacing correspond to modes while it increases between them. We have defined parametric, runs-based non-parametric, and data driven tests to evaluate these features --- flats and peaks --- and determine the presence and location of multiple modes. This report will check the tests in two situations. By varying a bi-modal setup we can control the changes in spacing and determine the resolution and sensitivity of the tests. By applying them to the large number of test cases that exist in the literature from other modality studies we check the stability and consistency of the results. We will also evaluate the accuracy of the null-distribution models of the features. The results show that the spacing does reflect the data's modality, that the tests do screen marginal cases, and where the analysis begins to break down.
Comments27 pages, 9 figures ; see MANIFEST for location of supplemental data ; v1 - added arXiv identifiers to references
