Two-sample test of high dimensional means under dependence

2013 ◽  
Vol 76 (2) ◽  
pp. 349-372 ◽  
Author(s):  
T. Tony Cai ◽  
Weidong Liu ◽  
Yin Xia
Keyword(s):  
High Dimensional ◽  
Sample Test

A note on high-dimensional two-sample test

2015 ◽  
Vol 105 ◽  
pp. 29-36 ◽  
Author(s):  
Long Feng ◽  
Fasheng Sun
Keyword(s):  
High Dimensional ◽  
Sample Test

High-dimensional Two-sample Test Procedures under the Strongly Spiked Eigenvalue Model

2020 ◽  
Vol 49 (3) ◽  
pp. 109-125
Author(s):  
Aki Ishii ◽  
Kazuyoshi Yata ◽  
Makoto Aoshima
Keyword(s):  
High Dimensional ◽  
Test Procedures ◽  
Sample Test

scholarly journals An adaptive two-sample test for high-dimensional means

Biometrika ◽  
2016 ◽  
Vol 103 (3) ◽  
pp. 609-624 ◽  
Author(s):  
Gongjun Xu ◽  
Lifeng Lin ◽  
Peng Wei ◽  
Wei Pan
Keyword(s):  
High Dimensional ◽  
Sample Test

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

scholarly journals A Two-Sample Test for Mean Vectors in High-Dimensional Data

2017 ◽  
Vol 1 (2) ◽  
pp. 118
Author(s):  
Knavoot Jiamwattanapong ◽  
Samruam Chongcharoen
Keyword(s):  
Sample Size ◽  
High Dimensional Data ◽  
Null Distribution ◽  
High Dimensional ◽  
Measurement Technology ◽  
Test Statistic ◽  
Dna Microarray Data ◽  
Statistical Inferences ◽  
Sample Test ◽  
Mean Vectors

<p><em>Modern measurement technology has enabled the capture of high-dimensional data by researchers and statisticians and classical statistical inferences, such as </em><em>the renowned Hotelling’s T<sup>2</sup> test, are no longer valid when the dimension of the data equals or exceeds the sample size. Importantly, when correlations among variables in a dataset exist, taking them into account in the analysis method would provide more accurate conclusions. In this article, we consider the hypothesis testing problem for two mean vectors in high-dimensional data with an underlying normality assumption. A new test is proposed based on the idea of keeping more information from the sample covariances. The asymptotic null distribution of the test statistic is derived. The simulation results show that the proposed test performs well comparing with other competing tests and becomes more powerful when the dimension increases for a given sample size. The proposed test is also illustrated with an analysis of DNA microarray data. </em></p>

scholarly journals Two-sample test for sparse high-dimensional multinomial distributions

Test ◽  
2018 ◽  
Vol 28 (3) ◽  
pp. 804-826 ◽  
Author(s):  
Amanda Plunkett ◽  
Junyong Park
Keyword(s):  
High Dimensional ◽  
Sample Test
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