Gaussian zonoids, Gaussian determinants and Gaussian random fields

We study the Vitale zonoid (a convex body related to a probability distribution) associated to a non-centered Gaussian vector. This defines a family of convex bodies, that contains and generalizes ellipsoids, which we call <italic>Gaussian zonoids</italic>. We show that each Gaussian zonoid can be approximated by an ellipsoid that we compute explicitely. We use this result to give new estimates for the expectation of the absolute value of the determinant of a non-centered Gaussian matrix in terms of mixed volume of ellipsoids. Finally, exploiting a recent link between random fields and zonoids uncovered by Stecconi and the author, we apply our results to the study of the zero sets of non-centered Gaussian random fields. We show how these can be approximated by a suitable centered Gaussian random field and give a quantitative asymptotic in the limit where the variance goes to zero.

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Publication Details

Journal
Theory of Probability and Mathematical Statistics
Published
2025-05-09
DOI
https://doi.org/10.1090/tpms/1229
Primary Topic
Statistical and numerical algorithms
Type
article
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article

Gaussian zonoids, Gaussian determinants and Gaussian random fields

Léo Mathis
Theory of Probability and Mathematical Statistics
Statistical and numerical algorithms
article

Gaussian zonoids, Gaussian determinants and Gaussian random fields

Léo Mathis
article en

Abstract

We study the Vitale zonoid (a convex body related to a probability distribution) associated to a non-centered Gaussian vector. This defines a family of convex bodies, that contains and generalizes ellipsoids, which we call <italic>Gaussian zonoids</italic>. We show that each Gaussian zonoid can be approximated by an ellipsoid that we compute explicitely. We use this result to give new estimates for the expectation of the absolute value of the determinant of a non-centered Gaussian matrix in terms of mixed volume of ellipsoids. Finally, exploiting a recent link between random fields and zonoids uncovered by Stecconi and the author, we apply our results to the study of the zero sets of non-centered Gaussian random fields. We show how these can be approximated by a suitable centered Gaussian random field and give a quantitative asymptotic in the limit where the variance goes to zero.

Theory of Probability and Mathematical StatisticsVol. 112(0)
Goethe University Frankfurt (DE)
Openalex Percentile: Top 86%
Statistical and numerical algorithms
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