论文标题
与自举层次结构的协方差矩阵过滤
Covariance matrix filtering with bootstrapped hierarchies
论文作者
论文摘要
对象之间依赖性的统计推断通常依赖于协方差矩阵。除非功能数量（例如数据点）比对象数量大得多，否则必须进行协方差矩阵清洁以减少估计噪声。我们提出了一种强大但灵活的方法，可以说明结构协方差矩阵的细节。鲁棒性来自使用分层ansatz和簇之间的依赖性。灵活性来自引导程序。该方法在DNA微阵列基因表达数据中发现了几个可能的层次结构，并且当数据点相对较小时，与当前过滤方法相比，全球最小差异投资组合中实现的风险较低。
Statistical inference of the dependence between objects often relies on covariance matrices. Unless the number of features (e.g. data points) is much larger than the number of objects, covariance matrix cleaning is necessary to reduce estimation noise. We propose a method that is robust yet flexible enough to account for fine details of the structure covariance matrix. Robustness comes from using a hierarchical ansatz and dependence averaging between clusters; flexibility comes from a bootstrap procedure. This method finds several possible hierarchical structures in DNA microarray gene expression data, and leads to lower realized risk in global minimum variance portfolios than current filtering methods when the number of data points is relatively small.
