1 citations · 1 across the 5 of their papers we have counts for
5 papers
High-Dimensional Two-Sample Inference via Marginal Likelihood-Ratio Rank Statistics
Xiaoxu Zhang, Long Feng
We develop a rank-based framework for high-dimensional two-sample testing that detects marginal distributional differences beyond means and variances. Three marginal likelihood-rat…
Rank-Based Tests for Mutual Independence of High-Dimensional Random Vectors via Norm
Ping Zhao, Hongfei Wang, Long Feng
We consider the problem of testing mutual independence among the components of a high-dimensional random vector. Building on the rank-based max-sum framework, we introduce fixed fi…
Testing Independence Between High-Dimensional Random Vectors Using Rank-Based Max-Sum Tests
Hongfei Wang, Binghui Liu, Long Feng
In this paper, we address the problem of testing independence between two high-dimensional random vectors. Our approach involves a series of max-sum tests based on three well-known…
Asymptotic Independence of the Sum and Maximum of Dependent Random Variables with Applications to High-Dimensional Tests
Long Feng, Tiefeng Jiang, Xiaoyun Li +1
For a set of dependent random variables, without stationary or the strong mixing assumptions, we derive the asymptotic independence between their sums and maxima. Then we apply thi…
Max-sum tests for cross-sectional dependence of high-demensional panel data
Long Feng, Tiefeng Jiang, Binghui Liu +1
We consider a testing problem for cross-sectional dependence for high-dimensional panel data, where the number of cross-sectional units is potentially much larger than the number o…