论文标题
回归设置中的高维数据分割允许时间依赖性和非高斯性
High-dimensional data segmentation in regression settings permitting temporal dependence and non-Gaussianity
论文作者
论文摘要
我们为高维线性回归问题提出了一种数据分割方法，其中允许回归参数经历多次更改。提出的方法（Moseg）分为两个阶段进行：首先，使用基于移动的窗口的过程对多个更改点进行扫描数据，然后是位置改进阶段。由于在第一阶段采用了粗网格，Moseg享有计算效率，并且在估算变化点的总数和位置方面达到了理论一致性，在允许串行依赖性和非高斯性的一般条件下。我们还提出了Moseg.ms，这是Moseg的多尺度延长，尽管在计算复杂性方面与Moseg相当，但对于更广泛的参数空间而言，可以同时在短时间间隔内进行大型参数变化，并且在长时间的平稳性中同时允许在长时间间隔内进行较大的参数变化。我们在比较模拟研究中表明了拟议方法的良好性能以及预测股票溢价的应用。
We propose a data segmentation methodology for the high-dimensional linear regression problem where regression parameters are allowed to undergo multiple changes. The proposed methodology, MOSEG, proceeds in two stages: first, the data are scanned for multiple change points using a moving window-based procedure, which is followed by a location refinement stage. MOSEG enjoys computational efficiency thanks to the adoption of a coarse grid in the first stage, and achieves theoretical consistency in estimating both the total number and the locations of the change points, under general conditions permitting serial dependence and non-Gaussianity. We also propose MOSEG.MS, a multiscale extension of MOSEG which, while comparable to MOSEG in terms of computational complexity, achieves theoretical consistency for a broader parameter space where large parameter shifts over short intervals and small changes over long stretches of stationarity are simultaneously allowed. We demonstrate good performance of the proposed methods in comparative simulation studies and in an application to predicting the equity premium.
