Localization Estimator for High Dimensional Tensor Covariance Matrices

Fuente: arXiv
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Hauptverfasser: Sun, Hao-Xuan, Chen, Song Xi, Qiu, Yumou
Format: Preprint
Veröffentlicht: 2026
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author Sun, Hao-Xuan
Chen, Song Xi
Qiu, Yumou
author_facet Sun, Hao-Xuan
Chen, Song Xi
Qiu, Yumou
contents This paper considers covariance matrix estimation of tensor data under high dimensionality. A multi-bandable covariance class is established to accommodate the need for complex covariance structures of multi-layer lattices and general covariance decay patterns. We propose a high dimensional covariance localization estimator for tensor data, which regulates the sample covariance matrix through a localization function. The statistical properties of the proposed estimator are studied by deriving the minimax rates of convergence under the spectral and the Frobenius norms. Numerical experiments and real data analysis on ocean eddy data are carried out to illustrate the utility of the proposed method in practice.
format Preprint
id arxiv_https___arxiv_org_abs_2601_06989
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Localization Estimator for High Dimensional Tensor Covariance Matrices
Sun, Hao-Xuan
Chen, Song Xi
Qiu, Yumou
Methodology
62H12
This paper considers covariance matrix estimation of tensor data under high dimensionality. A multi-bandable covariance class is established to accommodate the need for complex covariance structures of multi-layer lattices and general covariance decay patterns. We propose a high dimensional covariance localization estimator for tensor data, which regulates the sample covariance matrix through a localization function. The statistical properties of the proposed estimator are studied by deriving the minimax rates of convergence under the spectral and the Frobenius norms. Numerical experiments and real data analysis on ocean eddy data are carried out to illustrate the utility of the proposed method in practice.
title Localization Estimator for High Dimensional Tensor Covariance Matrices
topic Methodology
62H12
url https://arxiv.org/abs/2601.06989