论文标题
可变聚类通过分布强劲的节点回归
Variable Clustering via Distributionally Robust Nodewise Regression
论文作者
论文摘要
我们研究了用于可变聚类的多因素块模型，并通过制定刻度回归的分布稳健版本，将其连接到正则化子空间聚类。为了解决后一个问题，我们得出了凸松弛，为选择稳健区域的大小提供指导，因此根据数据提供了正则化权重参数，并提出了一种用于实现的ADMM算法。我们在广泛的模拟研究中验证了我们的方法。最后，我们提出并应用方法的一种方法来库存返回数据，获得可解释的群集，以促进投资组合选择，并将其除样性能与其他聚类方法进行比较。
We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression. To solve the latter problem, we derive a convex relaxation, provide guidance on selecting the size of the robust region, and hence the regularization weighting parameter, based on the data, and propose an ADMM algorithm for implementation. We validate our method in an extensive simulation study. Finally, we propose and apply a variant of our method to stock return data, obtain interpretable clusters that facilitate portfolio selection and compare its out-of-sample performance with other clustering methods in an empirical study.
