PreprintCC BY-ND 4.0HAL
UPPER TAIL PROBABILITY ESTIMATES OF SUPREMA OF GAUSSIAN PROCESSES BY MATRICES, A NUMBER THEORETIC EXAMPLE
Abstract
We prove new lower bounds for the upper tail probabilities of suprema of Gaussian processes. We introduce a proper matrix approach. This is continuing recent works of the author on deviations of suprema and decoupling inequalities for parametrized families of Gaussian processes. An application is given to a number theoretic example related to Wintner random Möbius function. j=1 1 ξ i .
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BibTeX
@misc{weber2026upper,
title = {{UPPER TAIL PROBABILITY ESTIMATES OF SUPREMA OF GAUSSIAN PROCESSES BY MATRICES, A NUMBER THEORETIC EXAMPLE}},
author = {Michel Weber},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26708},
eprint = {2610.26708},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05835913v1},
note = {CC BY-ND 4.0},
}
RIS
TY - UNPB
TI - UPPER TAIL PROBABILITY ESTIMATES OF SUPREMA OF GAUSSIAN PROCESSES BY MATRICES, A NUMBER THEORETIC EXAMPLE
AU - Michel Weber
PY - 2026
DA - 2026/10/19
PB - HAL
DO - 10.48550/arxiv.2610.26708
UR - https://hal.science/hal-05835913v1
AB - We prove new lower bounds for the upper tail probabilities of suprema of Gaussian processes. We introduce a proper matrix approach. This is continuing recent works of the author on deviations of suprema and decoupling inequalities for parametrized families of Gaussian processes. An application is given to a number theoretic example related to Wintner random Möbius function. j=1 1 ξ i .
ER -