An overview of tests on high-dimensional means
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 …
it is of great interest to test whether there is a difference of gene expressions between control …
Asymptotic independence of the sum and maximum of dependent random variables with applications to high-dimensional tests
L Feng, T Jiang, X Li, B Liu - arXiv preprint arXiv:2205.01638, 2022 - arxiv.org
For a set of dependent random variables, without stationary or the strong mixing
assumptions, we derive the asymptotic independence between their sums and maxima …
assumptions, we derive the asymptotic independence between their sums and maxima …
Adaptive inference for change points in high-dimensional data
Y Zhang, R Wang, X Shao - Journal of the American Statistical …, 2022 - Taylor & Francis
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 …
dimensional independent data. Our test integrates the U-statistic based approach in a recent …
A powerful fine-mapping method for transcriptome-wide association studies
Transcriptome-wide association studies (TWAS) have been recently applied to successfully
identify many novel genes associated with complex traits. While appealing, TWAS tend to …
identify many novel genes associated with complex traits. While appealing, TWAS tend to …
Adaptive testing for alphas in conditional factor models with high dimensional assets
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 …
specifically when the number of securities N is larger than the time dimension of the return …
Rank-based max-sum tests for mutual independence of high-dimensional random vectors
H Wang, B Liu, L Feng, Y Ma - Journal of Econometrics, 2024 - Elsevier
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 …
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
R Wang, X Shao - 2020 - projecteuclid.org
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 © …
Annals of Statistics 2020, Vol. 48, No. 5, 2728–2758 https://doi.org/10.1214/19-AOS1904 © …
Subspace recovery from heterogeneous data with non-isotropic noise
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 …
machine learning. Motivated by heterogeneity in Federated Learning settings, we study a …
Likelihood ratio tests under model misspecification in high dimensions
N Dörnemann - Journal of Multivariate Analysis, 2023 - Elsevier
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 …
increasing number of blocks under the null hypothesis. While so far the likelihood ratio …
Testing alpha in high dimensional linear factor pricing models with dependent observations
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 …
dimensional linear factor pricing model that deals with dependent data. The first method is a …