8 citations · 20 across the 11 of their papers we have counts for
6 papers · 1 filter
Dimension-agnostic Change Point Detection
Hanjia Gao, Runmin Wang, Xiaofeng Shao
Change point testing for high-dimensional data has attracted a lot of attention in statistics and machine learning owing to the emergence of high-dimensional data with structural b…
Robust Inference for Change Points in High Dimension
Feiyu Jiang, Runmin Wang, Xiaofeng Shao
This paper proposes a new test for a change point in the mean of high-dimensional data based on the spatial sign and self-normalization. The test is easy to implement with no tunin…
High-dimensional Change-point Detection Using Generalized Homogeneity Metrics
Shubhadeep Chakraborty, Runmin Wang, Xianyang Zhang
Change-point detection is a classical problem in statistics. We address the problem of detecting abrupt changes in the data-generating distributions of a sequence of high-dimension…
Adaptive Inference for Change Points in High-Dimensional Data
Yangfan Zhang, Runmin Wang, Xiaofeng Shao
In this article, we propose a class of test statistics for a change point in the mean of high-dimensional independent data. Our test integrates the U-statistic based approach in a…
Adaptive Change Point Monitoring for High-Dimensional Data
Teng Wu, Runmin Wang, Hao Yan +1
In this paper, we propose a class of monitoring statistics for a mean shift in a sequence of high-dimensional observations. Inspired by the recent U-statistic based retrospective t…
Dating the Break in High-dimensional Data
Runmin Wang, Xiaofeng Shao
This paper is concerned with estimation and inference for the location of a change point in the mean of independent high-dimensional data. Our change point location estimator maxim…