We consider the extreme eigenvalues of the sample covariance matrix Q=YY...
It is well known that most of the existing theoretical results in statis...
In this paper, we use the dimensional reduction technique to study the
c...
In this article, we first propose generalized row/column matrix Kendall'...
This paper proposes to test the number of common factors in high-dimensi...
Vegetation, trees in particular, sequester carbon by absorbing carbon di...
This paper focuses on the separable covariance matrix when the dimension...
We propose a new unsupervised learning method for clustering a large num...
An automated machine learning framework for geospatial data named PAIRS
...
Physics-informed neural networks (NN) are an emerging technique to impro...
One of the impacts of climate change is the difficulty of tree regrowth ...
Recent advances in object detection have benefited significantly from ra...
Microbial communities analysis is drawing growing attention due to the r...
In this paper, we propose a new statistical inference method for massive...
This paper considers the problem of resource allocation in stream proces...
Sample spatial-sign covariance matrix is a much-valued alternative to sa...
Let X be an M× N random matrices consisting of independent
M-variate ell...
Due to the increasing complexity of Integrated Circuits (ICs) and
System...
This paper discusses fluctuations of linear spectral statistics of
high-...
Sliced inverse regression (SIR) is the most widely-used sufficient dimen...
Linear regression is a fundamental and popular statistical method. There...