Covariance Operator Estimation: Sparsity, Lengthscale, and Ensemble Kalman Filters

Fuente: arXiv
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Hauptverfasser: Al-Ghattas, Omar, Chen, Jiaheng, Sanz-Alonso, Daniel, Waniorek, Nathan
Format: Preprint
Veröffentlicht: 2023
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author Al-Ghattas, Omar
Chen, Jiaheng
Sanz-Alonso, Daniel
Waniorek, Nathan
author_facet Al-Ghattas, Omar
Chen, Jiaheng
Sanz-Alonso, Daniel
Waniorek, Nathan
contents This paper investigates covariance operator estimation via thresholding. For Gaussian random fields with approximately sparse covariance operators, we establish non-asymptotic bounds on the estimation error in terms of the sparsity level of the covariance and the expected supremum of the field. We prove that thresholded estimators enjoy an exponential improvement in sample complexity compared with the standard sample covariance estimator if the field has a small correlation lengthscale. As an application of the theory, we study thresholded estimation of covariance operators within ensemble Kalman filters.
format Preprint
id arxiv_https___arxiv_org_abs_2310_16933
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Covariance Operator Estimation: Sparsity, Lengthscale, and Ensemble Kalman Filters
Al-Ghattas, Omar
Chen, Jiaheng
Sanz-Alonso, Daniel
Waniorek, Nathan
Statistics Theory
Probability
This paper investigates covariance operator estimation via thresholding. For Gaussian random fields with approximately sparse covariance operators, we establish non-asymptotic bounds on the estimation error in terms of the sparsity level of the covariance and the expected supremum of the field. We prove that thresholded estimators enjoy an exponential improvement in sample complexity compared with the standard sample covariance estimator if the field has a small correlation lengthscale. As an application of the theory, we study thresholded estimation of covariance operators within ensemble Kalman filters.
title Covariance Operator Estimation: Sparsity, Lengthscale, and Ensemble Kalman Filters
topic Statistics Theory
Probability
url https://arxiv.org/abs/2310.16933