6 papers
A new non-parametric test for multivariate paired data from pair matching or paired designs
Jingru Zhang, Hao Chen, Xiao-Hua Zhou
In observational studies, achieving covariate balance in pair matching between treatment and control groups or exposed and unexposed groups is essential. This balance enables testi…
Rank-Transformed Dissimilarity Profiles for High-Dimensional Classification
Xiangbo Mo, Hao Chen
Despite advances in representation learning, high-dimensional classification remains challenging in low-sample-size regimes, where the dominant signal may vary across applications…
High-Dimensional Clustering via Nearest-Neighbor Asymmetry
Hao Chen, Xiancheng Lin
High-dimensional clustering often relies on geometric or local-similarity structure, but the dominant separation between groups may not always be location-based. Differences in dis…
Asymptotic Distribution-free Change-point Detection for Modern Data Based on a New Ranking Scheme
Doudou Zhou, Hao Chen
Change-point detection (CPD) involves identifying distributional changes in a sequence of independent observations. Among nonparametric methods, rank-based methods are attractive d…
Mitigating dimensionality effects with robust graph constructions for testing
Yejiong Zhu, Hao Chen
Dimensionality effects pose major challenges in high-dimensional and non-Euclidean data analysis. Graph-based two-sample tests and change-point detection are particularly attractiv…
A fast and effective kernel two-sample test for large-scale data
Hoseung Song, Hao Chen
Kernel two-sample tests have been widely used, and the development of efficient methods for high-dimensional, large-scale data is receiving increasing attention in the big data era…