126 citations · 262 across the 32 of their papers we have counts for
32 papers
Change-point Inference for High-dimensional Heteroscedastic Data
Teng Wu, Stanislav Volgushev, Xiaofeng Shao
We propose a bootstrap-based test to detect a mean shift in a sequence of high-dimensional observations with unknown time-varying heteroscedasticity. The proposed test builds on th…
Two-Sample and Change-Point Inference for Non-Euclidean Valued Time Series
Feiyu Jiang, Changbo Zhu, Xiaofeng Shao
Data objects taking value in a general metric space have become increasingly common in modern data analysis. In this paper, we study two important statistical inference problems, n…
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…
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…
Testing Serial Independence of Object-Valued Time Series
Feiyu Jiang, Hanjia Gao, Xiaofeng Shao
We propose a novel method for testing serial independence of object-valued time series in metric spaces, which is more general than Euclidean or Hilbert spaces. The proposed method…
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…