Testing high-dimensional mean vector with applications

2021 ◽  
Author(s):  
Jin-Ting Zhang ◽  
Bu Zhou ◽  
Jia Guo
Keyword(s):  
High Dimensional ◽  
Mean Vector

scholarly journals Mean vector testing for high-dimensional dependent observations

2017 ◽  
Vol 153 ◽  
pp. 136-155 ◽  
Author(s):  
Deepak Nag Ayyala ◽  
Junyong Park ◽  
Anindya Roy
Keyword(s):  
High Dimensional ◽  
Dependent Observations ◽  
Mean Vector

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.

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