论文标题
将多元测试与多元奇异频谱分析相结合
Combining Multiple Testing with Multivariate Singular Spectrum Analysis
论文作者
论文摘要
适当的预处理是分析嘈杂数据集的基本先决条件。本文的目的是将一种称为奇异频谱分析（SSA）的非参数预处理方法应用于多种数据集中，随后通过多种统计假设检验对此进行了分析。 SSA是一种非参数预处理方法，最近在许多生命科学问题的背景下被使用。在目前的工作中，将SSA与其他三种最先进的预处理方法进行了比较，从denoising的好处和随后的多重测试的统计能力方面进行了比较。这些其他方法是参数或非参数。我们的发现表明，可以将（多元）SSA视为减少噪声，从噪声数据中提取主要信号并检测统计上显着的信号成分的有前途的方法。
Appropriate preprocessing is a fundamental prerequisite for analyzing a noisy dataset. The purpose of this paper is to apply a nonparametric preprocessing method, called Singular Spectrum Analysis (SSA), to a variety of datasets which are subsequently analyzed by means of multiple statistical hypothesis tests. SSA is a nonparametric preprocessing method which has recently been utilized in the context of many life science problems. In the present work, SSA is compared with three other state-of-the-art preprocessing methods in terms of goodness of denoising and in terms of the statistical power of the subsequent multiple test. These other methods are either parametric or nonparametric. Our findings demonstrate that (multivariate) SSA can be taken into account as a promising method to reduce noise, to extract the main signal from noisy data, and to detect statistically significant signal components.
