English

Maximal inequalities and weighted BMO processes

Probability 2022-12-21 v3

Abstract

For a general adapted integrable right-continuous with left limits (RCLL) process (Xt)t[0,τ](X_t)_{t\in[0,\tau]} taking values in a metric space (E,d)(\mathcal E,d), we show (among other things) that for every m(1,)m\in(1,\infty) m12m1supt[0,τ]E(d(Xt,Xτ)Ft)msupt[0,τ]d(X0,Xt)mcm2m1supt[0,τ]E(d(Xt,Xτ)Ft)m \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 cc. 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.

Keywords

Cite

@article{arxiv.2211.15550,
  title  = {Maximal inequalities and weighted BMO processes},
  author = {Khoa Lê},
  journal= {arXiv preprint arXiv:2211.15550},
  year   = {2022}
}

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update some details

R2 v1 2026-06-28T07:15:19.261Z