Change Detection in The Covariance Structure of High-Dimensional Gaussian Low-Rank Models

This paper is devoted to the problem of testing equality between the covariance matrices of L multivariate Gaussian time series with dimension M, in the context where each of the L covariance matrices is the sum of a low-rank K component and the identity matrix. Assuming N 1 , ..., N L samples are a...

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Bibliographic Details
Main Authors: Beisson, R., Vallet, P., Giremus, A., Ginolhac, G.
Format: Conference Proceeding
Language:English
Subjects:
Change detection
Conferences
covariance
Covariance matrices
Eigenvalues and eigenfunctions
Numerical models
Numerical simulation
random matrix theory
Signal processing
spiked models
Time series analysis
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Summary:This paper is devoted to the problem of testing equality between the covariance matrices of L multivariate Gaussian time series with dimension M, in the context where each of the L covariance matrices is the sum of a low-rank K component and the identity matrix. Assuming N 1 , ..., N L samples are available for each time series, a new test statistic, based on the eigenvalues of the L sample covariance matrices (SCM) of each time series as well as the eigenvalues of a pooled SCM mixing the N 1 +...+N L available samples, is pro-posed and proved to be consistent in the high dimensional regime in which M, N 1 , ..., N L converge to infinity at the same rate, while K and L are kept fixed. Numerical simulations show that the proposed test statistic is competitive with other relevant methods for moderate values of M, N 1 , ..., N L .
ISSN:2693-3551
DOI:10.1109/SSP49050.2021.9513795