Variable Clustering via Distributionally Robust Nodewise Regression
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio estimators when there are many assets in a p…
regressionTime Series AnalysisLARGE: A Locally Adaptive Regularization Approach for Estimating Gaussian Graphical Models
The graphical Lasso (GLASSO) is a widely used algorithm for learning high-dimensional undirected Gaussian graphical models (GGM). Given i.i.d. observations from a multivariate normal distribution, GLASSO estimates the pr…
Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models
This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than …
regressionTime SeriesVariable SelectionRobust Grouped Variable Selection Using Distributionally Robust Optimization
We propose a Distributionally Robust Optimization (DRO) formulation with a Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data for both linear regression and classification p…
ClusteringVariable SelectionA Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics. This limits uncertainty estimation on graphs since nodewi…
Node ClassificationStructured Prediction