scholarly journals Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure

2021 ◽  
Author(s):  
Anestis Touloumis ◽  
John C. Marioni ◽  
Simon Tavarè
Keyword(s):  
Hypothesis Testing ◽  
Covariance Matrix ◽  
Kronecker Product ◽  
High Dimensional ◽  
Dependence Structure ◽  
Transposable Data

scholarly journals Hypothesis testing on linear structures of high-dimensional covariance matrix

2019 ◽  
Vol 47 (6) ◽  
pp. 3300-3334 ◽  
Author(s):  
Shurong Zheng ◽  
Zhao Chen ◽  
Hengjian Cui ◽  
Runze Li
Keyword(s):  
Hypothesis Testing ◽  
Covariance Matrix ◽  
High Dimensional ◽  
Linear Structures

scholarly journals ON DETERMINING THE NUMBER OF SPIKES IN A HIGH-DIMENSIONAL SPIKED POPULATION MODEL

2012 ◽  
Vol 01 (01) ◽  
pp. 1150002 ◽  
Author(s):  
DAMIEN PASSEMIER ◽  
JIAN-FENG YAO
Keyword(s):  
Covariance Matrix ◽  
Population Model ◽  
Factor Model ◽  
Fundamental Problem ◽  
High Dimensional ◽  
Sample Covariance ◽  
Or Economics ◽  
Linear Mixture Model ◽  
The Difference ◽  
Scientific Fields

In a spiked population model, the population covariance matrix has all its eigenvalues equal to units except for a few fixed eigenvalues (spikes). Determining the number of spikes is a fundamental problem which appears in many scientific fields, including signal processing (linear mixture model) or economics (factor model). Several recent papers studied the asymptotic behavior of the eigenvalues of the sample covariance matrix (sample eigenvalues) when the dimension of the observations and the sample size both grow to infinity so that their ratio converges to a positive constant. Using these results, we propose a new estimator based on the difference between two consecutive sample eigenvalues.

scholarly journals On eigenvalues of a high-dimensional spatial-sign covariance matrix

Bernoulli ◽  
2022 ◽  
Vol 28 (1) ◽  
Author(s):  
Weiming Li ◽  
Qinwen Wang ◽  
Jianfeng Yao ◽  
Wang Zhou
Keyword(s):  
Covariance Matrix ◽  
High Dimensional
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