126 citations · 262 across the 32 of their papers we have counts for
14 papers · 1 filter
Adaptive Testing for High-dimensional Data
Yangfan Zhang, Runmin Wang, Xiaofeng Shao
In this article, we propose a class of -norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector…
Statistical inference for high-dimensional spectral density matrix
Jinyuan Chang, Qing Jiang, Tucker S. McElroy +1
The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component process…
Two Sample Testing in High Dimension via Maximum Mean Discrepancy
Hanjia Gao, Xiaofeng Shao
Maximum Mean Discrepancy (MMD) has been widely used in the areas of machine learning and statistics to quantify the distance between two distributions in the -dimensional Euclid…
Gaussian approximation and spatially dependent wild bootstrap for high-dimensional spatial data
Daisuke Kurisu, Kengo Kato, Xiaofeng Shao
In this paper, we establish a high-dimensional CLT for the sample mean of -dimensional spatial data observed over irregularly spaced sampling sites in , allowing t…
Inference for Change Points in High Dimensional Data via Self-Normalization
Runmin Wang, Changbo Zhu, Stanislav Volgushev +1
This article considers change point testing and estimation for a sequence of high-dimensional data. In the case of testing for a mean shift for high-dimensional independent data, w…
Distance-based and RKHS-based Dependence Metrics in High Dimension
Changbo Zhu, Shun Yao, Xianyang Zhang +1
In this paper, we study distance covariance, Hilbert-Schmidt covariance (aka Hilbert-Schmidt independence criterion [Gretton et al. (2008)]) and related independence tests under th…