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

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

scholarly journals JMASM 52: Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using R

2020 ◽  
Vol 18 (2) ◽  
pp. 2-16
Author(s):  
Christina Chatzipantsiou ◽  
Marios Dimitriadis ◽  
Manos Papadakis ◽  
Michail Tsagris
Keyword(s):  
Efficient Method ◽  
Statistical Tests ◽  
Pearson Correlation ◽  
Small Sample ◽  
Pearson Correlation Coefficient ◽  
Computationally Efficient ◽  
Sample Mean ◽  
P Values ◽  
Mean Vectors ◽  
Small Sample Sizes

Re-sampling based statistical tests are known to be computationally heavy, but reliable when small sample sizes are available. Despite their nice theoretical properties not much effort has been put to make them efficient. Computationally efficient method for calculating permutation-based p-values for the Pearson correlation coefficient and two independent samples t-test are proposed. The method is general and can be applied to other similar two sample mean or two mean vectors cases.

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.

scholarly journals On testing the equality of high dimensional mean vectors with unequal covariance matrices

2015 ◽  
Vol 69 (2) ◽  
pp. 365-387 ◽  
Author(s):  
Jiang Hu ◽  
Zhidong Bai ◽  
Chen Wang ◽  
Wei Wang
Keyword(s):  
Covariance Matrices ◽  
High Dimensional ◽  
Mean Vectors

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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