Common representations of functional networks of resting state fMRI time series, including
covariance, precision, and cross-correlation matrices, belong to the family of symmetric …

We introduce the so-called naive tests and give a brief review of the new developments.
Naive testing methods are easy to understand and perform robustly, especially when the …

Testing high-dimensional means has many applications in scientific research. For instance,
it is of great interest to test whether there is a difference of gene expressions between control …

High-dimensional low sample size (HDLSS) data are becoming increasingly common in
statistical applications. When the data can be partitioned into two classes, a basic task is to …

In this work, we generalize the Cramér–von Mises statistic via projection averaging to obtain
a robust test for the multivariate two-sample problem. The proposed test is consistent against …

We propose a multivariate generalization of the univariate two-sample run test based on the
shortest Hamiltonian path. The proposed test is distribution-free in finite samples. While most …

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown
strategy to contrast the spread of virus. Many studies have already documented that the …

We propose a two-sample test for detecting the difference between mean vectors in a high-
dimensional regime based on a ridge-regularized Hotelling's T ². To choose the …

A new test for equal distributions of several high-dimensional samples in separable metric
spaces, with its test statistic constructed based on maximum mean discrepancy, is proposed …

Peter Hall's work illuminated many aspects of statistical thought, some of which are very well
known including the bootstrap and smoothing. However, he also explored many other lesser …