scholarly journals Correction to: Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model

2021 ◽  
Author(s):  
Aki Ishii ◽  
Kazuyoshi Yata ◽  
Makoto Aoshima
Keyword(s):  
High Dimensional ◽  
Mean Vectors

The article Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model

An adaptive spatial-sign-based test for mean vectors of elliptically distributed high-dimensional data

2019 ◽  
Vol 12 (1) ◽  
pp. 93-106
Author(s):  
Bu Zhou ◽  
Jia Guo ◽  
Jianwei Chen ◽  
Jin-Ting Zhang
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Mean Vectors

High-dimensional two-sample mean vectors test and support recovery with factor adjustment

2020 ◽  
Vol 151 ◽  
pp. 107004
Author(s):  
Yong He ◽  
Mingjuan Zhang ◽  
Xinsheng Zhang ◽  
Wang Zhou
Keyword(s):  
High Dimensional ◽  
Sample Mean ◽  
Mean Vectors ◽  
Support Recovery

Ridgelized Hotelling’s T2 test on mean vectors of large dimension

2021 ◽  
pp. 2250011
Author(s):  
Gao-Fan Ha ◽  
Qiuyan Zhang ◽  
Zhidong Bai ◽  
You-Gan Wang
Keyword(s):  
Random Matrices ◽  
Pathway Analysis ◽  
Large Dimension ◽  
High Dimensional ◽  
Stat Assoc ◽  
Moment Conditions ◽  
Local Statistics ◽  
Mean Vectors ◽  
Simulation Results ◽  
Mean Vector

In this paper, a ridgelized Hotelling’s [Formula: see text] test is developed for a hypothesis on a large-dimensional mean vector under certain moment conditions. It generalizes the main result of Chen et al. [A regularized Hotelling’s [Formula: see text] test for pathway analysis in proteomic studies, J. Am. Stat. Assoc. 106(496) (2011) 1345–1360.] by relaxing their Gaussian assumption. This is achieved by establishing an exact four-moment theorem that is a simplified version of Tao and Vu’s [Random matrices: universality of local statistics of eigenvalues, Ann. Probab. 40(3) (2012) 1285–1315] work. Simulation results demonstrate the superiority of the proposed test over the traditional Hotelling’s [Formula: see text] test and its several extensions in high-dimensional situations.

Robust two-sample test of high-dimensional mean vectors under dependence

2019 ◽  
Vol 169 ◽  
pp. 312-329
Author(s):  
Wei Wang ◽  
Nan Lin ◽  
Xiang Tang
Keyword(s):  
High Dimensional ◽  
Sample Test ◽  
Mean Vectors
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