Open Access
2022 Kronecker-structured covariance models for multiway data
Yu Wang, Zeyu Sun, Dogyoon Song, Alfred Hero
Author Affiliations +
Statist. Surv. 16: 238-270 (2022). DOI: 10.1214/22-SS139


Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their inference. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.

Funding Statement

This work was supported in part by the SOLSTICE Drive Center funded by NASA and National Science Foundation under grant 80NSSC20K0600, the Army Research Office under grants W911NF1910269 and W911NF1510479, and by the National Nuclear Security Administration under grant DE-NA0003921.


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Yu Wang. Zeyu Sun. Dogyoon Song. Alfred Hero. "Kronecker-structured covariance models for multiway data." Statist. Surv. 16 238 - 270, 2022.


Received: 1 January 2022; Published: 2022
First available in Project Euclid: 15 December 2022

MathSciNet: MR4522372
zbMATH: 1502.62063
Digital Object Identifier: 10.1214/22-SS139

Primary: 60H15 , 60H20 , 62H12
Secondary: 62P12

Keywords: high dimensional statistics , multiway graphical Lasso , space weather applications , Tensor valued data

Vol.16 • 2022
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