论文标题
使用Hermite系列估计器对Spearman等级相关的顺序估计
Sequential estimation of Spearman rank correlation using Hermite series estimators
论文作者
论文摘要
在本文中，我们描述了针对Spearman等级相关系数的新型HERMITE系列估计器，并提供适用于固定和非平稳设置的算法。为了治疗非平稳设置，我们引入了Spearman等级相关性的新型，指数加权的估计器，该估计量允许跟踪双变量数据流的局部非参数相关性。据我们所知，这是提出第一种算法，以估计不依赖移动窗口方法的时间变化的Spearman等级相关性。我们通过实际数据和仿真研究探讨了基于Hermite系列的估计器的实际有效性，证明了良好的实践表现。仿真研究特别揭示了与现有算法相比的竞争性能。这项工作的潜在应用是多种多样的。基于HERMITE系列的Spearman等级相关估计器可以应用于可能随时间变化的快速，强大的在线计算。可能的机器学习应用程序包括在大规模数据集中进行快速功能选择和分层聚类。
In this article we describe a new Hermite series based sequential estimator for the Spearman rank correlation coefficient and provide algorithms applicable in both the stationary and non-stationary settings. To treat the non-stationary setting, we introduce a novel, exponentially weighted estimator for the Spearman rank correlation, which allows the local nonparametric correlation of a bivariate data stream to be tracked. To the best of our knowledge this is the first algorithm to be proposed for estimating a time varying Spearman rank correlation that does not rely on a moving window approach. We explore the practical effectiveness of the Hermite series based estimators through real data and simulation studies demonstrating good practical performance. The simulation studies in particular reveal competitive performance compared to an existing algorithm. The potential applications of this work are manifold. The Hermite series based Spearman rank correlation estimator can be applied to fast and robust online calculation of correlation which may vary over time. Possible machine learning applications include, amongst others, fast feature selection and hierarchical clustering on massive data sets.
