scholarly journals Asymptotic normality of sample covariance matrix for mixed spectra time series: Application to sinusoidal frequencies estimation

2001 ◽  
Vol 47 (4) ◽  
pp. 1681-1687 ◽  
Author(s):  
J.-P. Delmas
Keyword(s):  
Time Series ◽  
Asymptotic Normality ◽  
Covariance Matrix ◽  
Sample Covariance Matrix ◽  
Sample Covariance

ESTIMATION OF SPIKED EIGENVALUES IN SPIKED MODELS

2012 ◽  
Vol 01 (02) ◽  
pp. 1150011 ◽  
Author(s):  
ZHIDONG BAI ◽  
XUE DING
Keyword(s):  
Asymptotic Normality ◽  
Covariance Matrix ◽  
Sample Covariance Matrix ◽  
Sample Covariance ◽  
Consistency And Asymptotic Normality ◽  
Spiked Models

The paper proposes new estimators of spiked eigenvalues of the population covariance matrix from the sample covariance matrix and investigates its consistency and asymptotic normality.

Analysis of grid operation state based on improved maximum eigenvalue sample covariance matrix algorithm

2021 ◽  
pp. 095965182110012
Author(s):  
Dinghui Wu ◽  
Juan Zhang ◽  
Bo Wang ◽  
Tinglong Pan
Keyword(s):  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Sample Covariance Matrix ◽  
Maximum Eigenvalue ◽  
Signal To Noise ◽  
Matrix Algorithm ◽  
Sample Covariance ◽  
Noise Ratio ◽  
Application Fields

Traditional static threshold–based state analysis methods can be applied to specific signal-to-noise ratio situations but may present poor performance in the presence of large sizes and complexity of power system. In this article, an improved maximum eigenvalue sample covariance matrix algorithm is proposed, where a Marchenko–Pastur law–based dynamic threshold is introduced by taking all the eigenvalues exceeding the supremum into account for different signal-to-noise ratio situations, to improve the calculation efficiency and widen the application fields of existing methods. The comparison analysis based on IEEE 39-Bus system shows that the proposed algorithm outperforms the existing solutions in terms of calculation speed, anti-interference ability, and universality to different signal-to-noise ratio situations.

scholarly journals Estimation of the population spectral distribution from a large dimensional sample covariance matrix

2013 ◽  
Vol 143 (11) ◽  
pp. 1887-1897 ◽  
Author(s):  
Weiming Li ◽  
Jiaqi Chen ◽  
Yingli Qin ◽  
Zhidong Bai ◽  
Jianfeng Yao
Keyword(s):  
Covariance Matrix ◽  
Spectral Distribution ◽  
Sample Covariance Matrix ◽  
Sample Covariance
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