1 citations · 3 across the 14 of their papers we have counts for
7 papers · 1 filter
Panel Coupled Matrix-Tensor Clustering Model with Applications to Asset Pricing
Liyuan Cui, Guanhao Feng, Yuefeng Han +1
We tackle the challenge of estimating grouping structures and factor loadings in asset pricing models, where traditional regressions struggle due to sparse data and high noise. Exi…
High-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
Elynn Chen, Yuefeng Han, Jiayu Li
High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on…
Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
Lingchong Liu, Elynn Chen, Yuefeng Han +1
Biochemical discovery increasingly relies on classifying molecular structures when the consequences of different errors are highly asymmetric. In mutagenicity and carcinogenicity,…
Modewise Additive Factor Model for Matrix Time Series
Elynn Chen, Yuefeng Han, Jiayu Li +1
We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offe…
Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
Ke Xu, Yuefeng Han
Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, tensor regression, a…
WISE: A Weighted Similarity Aggregation Test for Serial Independence
Qihua Zhu, Mingshuo Liu, Yuefeng Han +1
We propose a nonparametric test for serial independence that aggregates pairwise similarities of observations with lag-dependent weights. The resulting statistic is powerful to gen…
Statistical Inference for Low-Rank Tensor Models
Ke Xu, Elynn Chen, Yuefeng Han
Statistical inference for tensors has emerged as a critical challenge in analyzing high-dimensional data in modern data science. This paper introduces a unified framework for infer…