We consider the problem of quantifying uncertainty for the estimation error of the leading
eigenvector from Oja's algorithm for streaming principal component analysis, where the data …

In this paper, we develop a systematic theory for high-dimensional analysis of variance in
multivariate linear regression, where the dimension and the number of coefficients can both …

This paper is concerned with testing global null hypotheses about population mean vectors
of high-dimensional data. Current tests require either strong mixing (independence) …

We prove the large-dimensional Gaussian approximation of a sum of $ n $ independent
random vectors in $\mathbb {R}^ d $ together with fourth-moment error bounds on convex …

Wilks' theorem, which offers universal chi-squared approximations for likelihood ratio tests,
is widely used in many scientific hypothesis testing problems. For modern datasets with …

We prove the large-dimensional Gaussian approximation of a sum of n independent random
vectors in R d together with fourth-moment error bounds on convex sets and Euclidean balls …

Abstract The Drop Size Distribution (DSD) has been modelled, and the dataset is being fitted
using exponential, gamma, and lognormal distribution approaches. The existing Gaussian …

Digitalization of BSR (buyer supplier relationship) is generally one of the effective tools to
strengthen the supply chain. This study aims to establish the correlations between the …

The Cramér–von Mises test provides a useful criterion for assessing goodness of fit in
various problems. In this paper, we introduce a novel Cramér–von Mises-type test for testing …

It can be argued that optimal prediction should take into account all available data.
Therefore, to evaluate a prediction interval's performance one should employ conditional …