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 …

For a set of dependent random variables, without stationary or the strong mixing
assumptions, we derive the asymptotic independence between their sums and maxima …

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 recent …

Transcriptome-wide association studies (TWAS) have been recently applied to successfully
identify many novel genes associated with complex traits. While appealing, TWAS tend to …

This article focuses on testing for the presence of alpha in time-varying factor pricing models,
specifically when the number of securities N is larger than the time dimension of the return …

We consider the problem of testing mutual independence of high-dimensional random
vectors, and propose a series of high-dimensional rank-based max-sum tests, which are …

Hypothesis testing for high-dimensional time series via self-normalization Page 1 The
Annals of Statistics 2020, Vol. 48, No. 5, 2728–2758 https://doi.org/10.1214/19-AOS1904 © …

Recovering linear subspaces from data is a fundamental and important task in statistics and
machine learning. Motivated by heterogeneity in Federated Learning settings, we study a …

We investigate the likelihood ratio test for a large block-diagonal covariance matrix with an
increasing number of blocks under the null hypothesis. While so far the likelihood ratio …

In this study, we introduce three distinct testing methods for testing alpha in high
dimensional linear factor pricing model that deals with dependent data. The first method is a …