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 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 test, are no longer valid when the dimension of the data equals or exceeds the sample size.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>

Test on the linear combinations of mean vectors in high-dimensional data

Test ◽  
2016 ◽  
Vol 26 (1) ◽  
pp. 188-208 ◽  
Author(s):  
Huiqin Li ◽  
Jiang Hu ◽  
Zhidong Bai ◽  
Yanqing Yin ◽  
Kexin Zou
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Linear Combinations ◽  
Mean Vectors

scholarly journals Diagonal likelihood ratio test for equality of mean vectors in high‐dimensional data

Biometrics ◽  
2019 ◽  
Vol 75 (1) ◽  
pp. 256-267
Author(s):  
Zongliang Hu ◽  
Tiejun Tong ◽  
Marc G. Genton
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Ratio Test ◽  
Mean Vectors ◽  
Equality Of Mean Vectors

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

Approximate Cluster Heat Maps of Large High-Dimensional Data

2018 ◽  
Author(s):  
Punit Rathore ◽  
James C. Bezdek ◽  
Dheeraj Kumar ◽  
Sutharshan Rajasegarar ◽  
Marimuthu Palaniswami
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Heat Maps
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