scholarly journals Test of independence for high-dimensional random vectors based on freeness in block correlation matrices

2017 ◽  
Vol 11 (1) ◽  
pp. 1527-1548 ◽  
Author(s):  
Zhigang Bao ◽  
Jiang Hu ◽  
Guangming Pan ◽  
Wang Zhou
Keyword(s):  
High Dimensional ◽  
Random Vectors ◽  
Correlation Matrices ◽  
Test Of Independence

scholarly journals Sign test of independence between two random vectors

2003 ◽  
Vol 62 (1) ◽  
pp. 9-21 ◽  
Author(s):  
Sara Taskinen ◽  
Annaliisa Kankainen ◽  
Hannu Oja
Keyword(s):  
Sign Test ◽  
Random Vectors ◽  
Test Of Independence

scholarly journals High Dimensional Correlation Matrices: CLT and Its Applications

2014 ◽  
Author(s):  
Jiti Gao ◽  
Xiao Han ◽  
Guangming Pan ◽  
Yanrong Yang
Keyword(s):  
High Dimensional ◽  
Correlation Matrices

scholarly journals Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors

2013 ◽  
Vol 41 (6) ◽  
pp. 2786-2819 ◽  
Author(s):  
Victor Chernozhukov ◽  
Denis Chetverikov ◽  
Kengo Kato
Keyword(s):  
High Dimensional ◽  
Random Vectors

Sparse estimation of high-dimensional correlation matrices

2016 ◽  
Vol 93 ◽  
pp. 390-403 ◽  
Author(s):  
Ying Cui ◽  
Chenlei Leng ◽  
Defeng Sun
Keyword(s):  
High Dimensional ◽  
Sparse Estimation ◽  
Correlation Matrices

scholarly journals Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models

2021 ◽  
Author(s):  
David Morales-Jimenez ◽  
Iain M. Johnstone ◽  
Matthew R. McKay ◽  
Jeha Yang
Keyword(s):  
High Dimensional ◽  
Correlation Matrices ◽  
Sample Correlation ◽  
Spiked Models

scholarly journals Limit Properties of the Largest Entries of High-Dimensional Sample Covariance and Correlation Matrices

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xue Ding
Keyword(s):  
Sample Size ◽  
Covariance Matrices ◽  
High Dimensional ◽  
Extreme Statistics ◽  
Correlation Matrices ◽  
Underlying Distribution ◽  
Sample Covariance Matrices ◽  
Sample Covariance ◽  
Sample Correlation

In this paper, we consider the limit properties of the largest entries of sample covariance matrices and the sample correlation matrices. In order to make the statistics based on the largest entries of the sample covariance matrices and the sample correlation matrices more applicable in high-dimensional tests, the identically distributed assumption of population is removed. Under some moment’s assumption of the underlying distribution, we obtain that the almost surely limit and asymptotical distribution of the extreme statistics as both the dimension p and sample size n tend to infinity.

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