Random Martingales and localization of maximal inequalities

TL;DR

This paper establishes a localization principle for maximal functions on metric measure spaces with microdoubling, linking their weak (1,1) bounds to localized versions, and demonstrates sharpness of bounds in Ahlfors-David regular spaces.

Contribution

It introduces a localization property for Hardy-Littlewood maximal operators under microdoubling conditions and proves sharp bounds in Ahlfors-David regular spaces, extending classical results.

Findings

Localization of maximal inequalities under microdoubling

Weak (1,1) bounds grow as n log n in Ahlfors-David regular spaces

Constructed examples show bounds are sharp

Abstract

Let $(X,d,\mu)$ be a metric measure space. For $\emptyset\neq R\subseteq (0,\infty)$ consider the Hardy-Littlewood maximal operator $$ M_R f(x) \stackrel{\mathrm{def}}{=} \sup_{r \in R} \frac{1}{\mu(B(x,r))} \int_{B(x,r)} |f| d\mu.$$ We show that if there is an $n>1$ such that one has the "microdoubling condition" $ \mu(B(x,(1+\frac{1}{n})r))\lesssim \mu(B(x,r)) $ for all $x\in X$ and $r>0$, then the weak $(1,1)$ norm of $M_R$ has the following localization property: $$ \|M_R\|_{L_1(X) \to L_{1,\infty}(X)}\asymp \sup_{r>0} \|M_{R\cap [r,nr]}\|_{L_1(X) \to L_{1,\infty}(X)}. $$ An immediate consequence is that if $(X,d,\mu)$ is Ahlfors-David $n$-regular then the weak $(1,1)$ norm of $M_R$ is $\lesssim n\log n$, generalizing a result of Stein and Str\"omberg. We show that this bound is sharp, by constructing a metric measure space $(X,d,\mu)$ that is Ahlfors-David $n$-regular, for which the weak $(1,1)$ norm of $M_{(0,\infty)}$ is $\gtrsim n\log n$. The localization property of $M_R$ is proved by assigning to each $f\in L_1(X)$ a distribution over {\em random} martingales for which the associated (random) Doob maximal inequality controls the weak $(1,1)$ inequality for $M_R$.