Predictable subordination, sharp martingale inequalities and applications
Abstract
We introduce a new method for obtaining sharp Lp estimates for martingales under predictable analogues of differential subordination. By allowing suitable comparisons between continuous and jump variation adapted to the relevant range of p, we retain sharp constants in settings where pathwise differential subordination fails. The key idea is to study together the continuous and jump contributions arising from the appropriate Bellman functions. Although these contributions need not be nonpositive separately, we quantify their defects and show that they compensate at the predictable level. This compensation mechanism is inspired by the author's earlier work. As an application, we substantially improve the explicit dimension-free bounds for the Riesz vectors on the Hamming cube and on ℤn.
Related
- Lp maximal inequalities for Gaussian subordination with applications to Breuer--Major--Donsker principles
- Classification of solutions to the Liouville equation with a nonlinear Neumann boundary condition and applications to sharp inequalities
- On sharp singular Moser-Trudinger type inequalities and applications to zero mass (p,Q)-Laplace subelliptic equations in the Heisenberg group
- Sharp Subordination Radii through Boundary Minimization
- Finite-sample guarantees for data-driven operator splitting methods via martingale inequalities
BibTeX
@misc{demilio2026predictable,
title = {{Predictable subordination, sharp martingale inequalities and applications}},
author = {Francesco D'Emilio},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12238},
eprint = {2609.12238},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12238},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Predictable subordination, sharp martingale inequalities and applications
AU - Francesco D'Emilio
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.12238
UR - https://arxiv.org/abs/2609.12238
AB - We introduce a new method for obtaining sharp L^p estimates for martingales under predictable analogues of differential subordination. By allowing suitable comparisons between continuous and jump variation adapted to the relevant range of p, we retain sharp constants in settings where pathwise differential subordination fails. The key idea is to study together the continuous and jump contributions arising from the appropriate Bellman functions. Although these contributions need not be nonpositive separately, we quantify their defects and show that they compensate at the predictable level. This compensation mechanism is inspired by the author's earlier work. As an application, we substantially improve the explicit dimension-free bounds for the Riesz vectors on the Hamming cube and on ℤ^n.
ER -