English

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 p>2p>2. 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}
}

Comments

15 pages