One of the significant challenges in monitoring the quality of products today is the high
dimensionality of quality characteristics. In this paper, we address Phase I analysis of high …

Testing the equality of the covariance matrices of two high-dimensional samples is a
fundamental inference problem in statistics. Several tests have been proposed but they are …

The challenge of location testing for high-dimensional data in statistical inference is notable.
Existing literature suggests various methods, many of which impose strong regularity …

In this paper, we propose a new scale-invariant test for linear hypothesis of mean vectors
with heteroscedasticity in high-dimensional settings. Most existing tests impose strong …

In this paper, we propose a new scalar-transformation-invariant test for linear hypothesis on
mean vectors of normal population with unequal covariance matrices in high-dimensional …

For high-dimensional two-sample Behrens–Fisher problems, several non-scale-invariant
and scale-invariant tests have been proposed. Most of them impose strong assumptions on …

Hotelling's T-squared test is a classical tool to test if the normal mean of a multivariate
normal distribution is a specified one or the means of two multivariate normal means are …

Modern data collecting methods and computation tools have made it possible to monitor
high‐dimensional processes. In this article, we investigate phase II monitoring of high …

Recently, several non-scale-invariant and scale-invariant tests have been proposed for a
general linear hypothesis testing problem for high-dimensional data, which include one-way …

Bai–Saranadasa's two-sample test for high-dimensional data, namely BS-test, has been
widely cited in the literature. However, it may not control the size well when the required …