High Excursion Probabilities for Gaussian Fields on Smooth Manifolds

Gaussian random fields on finite-dimensional smooth manifolds, whose variance functions reach their maximum values at smooth submanifolds, are considered, and the exact asymptotic behavior of large excursion probabilities is established. It is shown that our conditions on the behavior of the covariation and variance are best possible in the context of the classical Pickands double sum method. Applications of our asymptotic formulas to large deviations of Gaussian vector processes are considered, and some examples are given. This paper continues the previous study of the author with Kobelkov, Rodionov, and Hashorva [J. Math. Sci., 262 (2022), pp. 504--513] which was concerned with Gaussian processes and fields on manifolds with a single point of maximum of the variance.

Authors

Publication Details

Journal
Theory of Probability and Its Applications
Published
2024-08-01
DOI
https://doi.org/10.1137/s0040585x97t991921
Citations
1
Primary Topic
Stochastic processes and statistical mechanics
Type
article
Field-Weighted Citation Impact
0.89
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High Excursion Probabilities for Gaussian Fields on Smooth Manifolds

V. I. Piterbarg
1 citations
Theory of Probability and Its Applications
Stochastic processes and statistical mechanics
0.89
article

High Excursion Probabilities for Gaussian Fields on Smooth Manifolds

V. I. Piterbarg
article en
1 citations

Abstract

Gaussian random fields on finite-dimensional smooth manifolds, whose variance functions reach their maximum values at smooth submanifolds, are considered, and the exact asymptotic behavior of large excursion probabilities is established. It is shown that our conditions on the behavior of the covariation and variance are best possible in the context of the classical Pickands double sum method. Applications of our asymptotic formulas to large deviations of Gaussian vector processes are considered, and some examples are given. This paper continues the previous study of the author with Kobelkov, Rodionov, and Hashorva [J. Math. Sci., 262 (2022), pp. 504--513] which was concerned with Gaussian processes and fields on manifolds with a single point of maximum of the variance.

Theory of Probability and Its ApplicationsVol. 69(2)
Openalex Percentile: Top 31%
Stochastic processes and statistical mechanics
0.89
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.