 A high-dimensional spatial rank test for two-sample location problemsLong Feng, Xiaoxu Zhang and Binghui Liu Computational Statistics & Data Analysis, 2020, vol. 144, issue C Abstract: In high-dimensional situations, the traditional multivariate sign- or rank-based procedures for the two-sample location testing problems are ineffective, since in the construction of the test statistics, the scatter matrix to be inverted is singular. To solve this problem, many high-dimensional spatial sign or rank tests have been proposed, some of which are very efficient. However, most of these existing tests no longer work in very high dimensional situations, which only allows the dimension of variables to be the square of the sample sizes at most, hence are restrictive for practical applications. On this ground, a new high-dimensional spatial rank test is proposed in this paper, which is invariant under scalar transformations, maintains the efficiency advantage of spatial-rank-based testing methods, and could even allow the dimension to grow almost exponentially with the sample sizes. The theoretical results of the proposed test are established, followed by some convincing numerical results and two real data analyses. Keywords: High-dimensional; Scalar-invariant; Spatial rank; Two-sample location problems (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302440 DOI: 10.1016/j.csda.2019.106889 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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