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 …

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.
However, existing uncertainty estimation approaches rely on low-dimensional distributional …

In this paper, we give the most current account of methods for comparison of populations or
treatment groups with high-dimensional data. We conveniently group the methods into three …

Meta-learning involves training models on a variety of training tasks in a way that enables
them to generalize well on new, unseen test tasks. In this work, we consider meta-learning …

Testing the equality of the means in two samples is a fundamental statistical inferential
problem. Most of the existing methods are based on the sum-of-squares or supremum …

Nocebo effects on pain are widely thought to be driven by negative expectations. This
suggests that anticipatory processing, or some other form of top-down cognitive activity prior …

In this work, we consider the problem of goodness-of-fit (GoF) testing for parametric models--
for example, testing whether observed data follows a logistic regression model. This testing …

The human microbiome is a dynamic system that changes due to diseases, medication,
change in diet, etc. The paired design is a common approach to evaluate the microbial …

The Wilcoxon signed-rank test and the Wilcoxon-Mann-Whitney test are two commonly used
rank-based methods for one-and two-sample tests when the one-dimensional data are not …

In this paper, we discuss tests for mean vector of high-dimensional data when the dimension
$ p $ is a function of sample size $ n $. One of the tests, called the decomposite $ T^{2} …