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 …

This article not only presents a test based on the well-known Box M test for testing the
equality of several covariance matrices with high-dimensional data, but also gives the …

Banding the inverse of a covariance matrix has become a popular technique for estimating a
covariance matrix from a limited number of samples. It is of interest to provide criteria to …

By collecting multiple sets per subject in microarray data, gene sets analysis requires
characterize intra-subject variation using gene expression profiling. For each subject, the …

The main motivation of this manuscript is to deepen our understanding of phenomena with a
temporal component. Most machine learning algorithms and high-dimensional statistical …

The objective of this thesis is to model matrix-valued time series in a high-dimensional
framework. To this end, the entire study is presented in a non-asymptotic framework. We first …

We propose a test for testing the equality of several high-dimensional covariance matrices
for stationary processes with a general distribution. The asymptotic distribution of the …

Testing high-dimensional covariance matrix plays an important role in multivariate statistical
analysis. Many statisticians used the statistics based on tr [(Σ Σ 0− 1− I p) 2] to test H 0: Σ= Σ …

Testing sphericity of the covariance matrices has been an active part in contemporary
statistics. In this paper, we put forward a new test procedure for high-dimensional sphericity …

Testing the existence of low-dimensional perturbations or signals is very important, eg, in
factor analysis and signal processing. This paper aims to develop new tests for high …