On the Estimation of Gaussian Moment Tensors
Statistics Theory
2025-10-29 v2 Probability
Statistics Theory
Abstract
This paper studies two estimators for Gaussian moment tensors: the standard sample moment estimator and a plug-in estimator based on Isserlis's theorem. We establish dimension-free, non-asymptotic error bounds that demonstrate and quantify the advantage of Isserlis's estimator for tensors of even order . Our bounds hold in operator and entrywise maximum norms, and apply to symmetric and asymmetric tensors.
Keywords
Cite
@article{arxiv.2507.06166,
title = {On the Estimation of Gaussian Moment Tensors},
author = {Omar Al-Ghattas and Jiaheng Chen and Daniel Sanz-Alonso},
journal= {arXiv preprint arXiv:2507.06166},
year = {2025}
}
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15 pages