136 citations · 239 across the 24 of their papers we have counts for
24 papers
Cursive: The Trace from the Curse of Dimensionality
Hao Chen
Modern data are increasingly high-dimensional or non-Euclidean. As dimension grows, new statistical patterns can emerge in the relations among observations, while a conventional st…
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…
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…
On the tightness of graph-based statistics
Lynna Chu, Hao Chen
We establish tightness of graph-based stochastic processes in the space with that allows for discontinuities of the first kind. The graph-based stochastic proces…
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…
Practical and powerful kernel-based change-point detection
Hoseung Song, Hao Chen
Change-point analysis plays a significant role in various fields to reveal discrepancies in distribution in a sequence of observations. While a number of algorithms have been propo…
New graph-based multi-sample tests for high-dimensional and non-Euclidean data
Hoseung Song, Hao Chen
Testing the equality in distributions of multiple samples is a common task in many fields. However, this problem for high-dimensional or non-Euclidean data has not been well explor…
A new ranking scheme for modern data and its application to two-sample hypothesis testing
Doudou Zhou, Hao Chen
Rank-based approaches are among the most popular nonparametric methods for univariate data in tackling statistical problems such as hypothesis testing due to their robustness and e…
Graph-based multiple change-point detection
Yuxuan Zhang, Hao Chen
We propose a new multiple change-point detection framework for multivariate and non-Euclidean data. First, we combine graph-based statistics with wild binary segmentation or seeded…
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…
A note on the permutation distribution of generalized correlation coefficients
Yejiong Zhu, Hao Chen
We provide sufficient conditions for the asymptotic normality of the generalized correlation coefficient under the permutation null distribution when 's…
Limiting distributions of graph-based test statistics on sparse and dense graphs
Yejiong Zhu, Hao Chen
Two-sample tests utilizing a similarity graph on observations are useful for high-dimensional and non-Euclidean data due to their flexibility and good performance under a wide rang…
Generalized Kernel Two-Sample Tests
Hoseung Song, Hao Chen
Kernel two-sample tests have been widely used for multivariate data to test equality of distributions. However, existing tests based on mapping distributions into a reproducing ker…
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…
A Fast and Efficient Change-point Detection Framework based on Approximate -Nearest Neighbor Graphs
Yi-Wei Liu, Hao Chen
Change-point analysis is thriving in this big data era to address problems arising in many fields where massive data sequences are collected to study complicated phenomena over tim…
Asymptotic distribution-free change-point detection for data with repeated observations
Hoseung Song, Hao Chen
In the regime of change-point detection, a nonparametric framework based on scan statistics utilizing graphs representing similarities among observations is gaining attention due t…
Sequential Change-point Detection for High-dimensional and non-Euclidean Data
Lynna Chu, Hao Chen
In many applications, it is often of practical and scientific interest to detect anomaly events in a streaming sequence of high-dimensional or non-Euclidean observations. We study…
Graph-Based Two-Sample Tests for Data with Repeated Observations
Jingru Zhang, Hao Chen
In the regime of two-sample comparison, tests based on a graph constructed on observations by utilizing similarity information among them is gaining attention due to their flexibil…
Asymptotic Distribution-Free Change-Point Detection for Multivariate and non-Euclidean Data
Lynna Chu, Hao Chen
We consider the testing and estimation of change-points, locations where the distribution abruptly changes, in a sequence of multivariate or non-Euclidean observations. We study a…
A weighted edge-count two-sample test for multivariate and object data
Hao Chen, Xu Chen, Yi Su
Two-sample tests for multivariate data and non-Euclidean data are widely used in many fields. Parametric tests are mostly restrained to certain types of data that meets the assumpt…
Sequential change-point detection based on nearest neighbors
Hao Chen
We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequence…
A new graph-based two-sample test for multivariate and object data
Hao Chen, Jerome H. Friedman
Two-sample tests for multivariate data and especially for non-Euclidean data are not well explored. This paper presents a novel test statistic based on a similarity graph construct…
Graph-Based Change-Point Detection
Hao Chen, Nancy Zhang
We consider the testing and estimation of change-points -- locations where the distribution abruptly changes -- in a data sequence. A new approach, based on scan statistics utilizi…