Robust two-sample test of high-dimensional mean vectors under dependence
Robust two-sample test of high-dimensional mean vectors under dependence is a scholarly work, published in 2019 in ''Journal of Multivariate Analysis''. The main subjects of the publication include statistical test, regularization, robust statistics, multicollinearity, biological robustness, graph dimension, statistics, statistic, estimator, statistical process control, truncated mean, test statistic, mathematics, sample size determination, Gumbel distribution, and multivariate statistics. The paper proposes a robust two-sample test for high-dimensional data against sparse and strong alternatives, in which the mean vectors of the populations differ in only a few dimensions, but the magnitude of the differences is large.