Maximal inequalities and weighted BMO processes

TL;DR

This paper establishes maximal inequalities for adapted RCLL processes in metric spaces, connecting probabilistic estimates with BMO processes and providing a unified approach via John--Nirenberg inequalities.

Contribution

It introduces new inequalities for weighted BMO processes and applies them to martingale estimates, extending classical results with a unified framework.

Findings

Derived inequalities relate supremum norms and conditional expectations.

Established a probabilistic version of Fefferman--Stein estimate.

Connected BMO process inequalities with martingale theory.

Abstract

For a general adapted integrable right-continuous with left limits (RCLL) process $(X_t)_{t\in[0,\tau]}$ taking values in a metric space $(\mathcal E,d)$, we show (among other things) that for every $m\in(1,\infty)$ $$ \frac{m-1}{2m-1}\|\sup_{t\in[0,\tau]}\mathbb{E}(d(X_{t-},X_\tau)|\mathcal F_t)\|_m\le \|\sup_{t\in[0,\tau]}d(X_0,X_t)\|_m\le c\frac{m^2}{m-1} \|\sup_{t\in[0,\tau]}\mathbb{E}(d(X_{t-},X_\tau)|\mathcal F_t)\|_m $$ with a universal constant $c$. This is a probabilistic version of Fefferman--Stein estimate for the sharp maximal functions. While the former inequality is derived easily from Doob's martingale inequality, the later inequality is a consequence of John--Nirenberg inequalities for weighted BMO processes, which are obtained in this note. We explain how John--Nirenberg inequalities can be utilized to obtain inequalities for martingales, both old and new alike in a unified way.