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Source. Erdős–Taylor (1960), Theorem 13, printed pp. 161–162 (PDF pp. 25–26). This proof expands the source's union bound and independent-piece argument, with integer cutoffs and an explicit all-future consequence used in Hao–Li–Okada–Zheng, Lemma 3.2.

For symmetric nearest-neighbor simple random walk on Zd\mathbb Z^d, d≥3d\ge3, write ξ(x,n)=∑j=0n1{Sj=x}\xi(x,n)=\sum_{j=0}^n1_{\{S_j=x\}} and ξ∗(n)=max⁡xξ(x,n)\xi^*(n)=\max_x\xi(x,n). Let γ∈(0,1)\gamma\in(0,1) be the probability of never returning to zero after time zero, q=1−γq=1-\gamma, and α=−1/log⁡q\alpha=-1/\log q. Then, almost surely,

ξ∗(n)log⁡n⟶α.(1)\frac{\xi^*(n)}{\log n}\longrightarrow\alpha. \tag{1}

More quantitatively, for every 0<ϵ<10<\epsilon<1, define

Dnϵ={(1−ϵ/2)αlog⁡n≤ξ∗(n)≤(1+ϵ/2)αlog⁡n}.D_n^\epsilon= \{(1-\epsilon/2)\alpha\log n\le\xi^*(n) \le(1+\epsilon/2)\alpha\log n\}.

There are C=C(d,ϵ)C=C(d,\epsilon) and N0=N0(d,ϵ)N_0=N_0(d,\epsilon) such that

P(∃n≥N:(Dnϵ)c)≤CN−ϵ/4(N≥N0).(2)\mathbb P(\exists n\ge N:(D_n^\epsilon)^c) \le C N^{-\epsilon/4}\qquad(N\ge N_0). \tag{2}

Classical input. The only analytic estimate needed below is sup⁡xP(Sj=x)≤Cdj−d/2\sup_x\mathbb P(S_j=x)\le C_dj^{-d/2} for j≥1j\ge1. This is the standard transient heat-kernel estimate. The summation proof in Hao–Li–Okada–Zheng, Lemma 2.2 gives P(∃j>h:Sj=0)≤Cdh1−d/2\mathbb P(\exists j>h:S_j=0)\le C_dh^{1-d/2}. No conclusion of Hao's Theorem 1.2 is used.

Upper tail. Starting at a site, the number of visits to that site, including the starting visit, has tail qr−1q^{r-1} at integer threshold r≥1r\ge1, by the strong Markov property at successive returns. If a site has local time at least rr by time nn, its first visit occurs at some i∈{0,…,n}i\in\{0,\ldots,n\} and the walk from ii makes at least rr visits to SiS_i. Discarding the first-visit restriction only enlarges the event. A union bound and the independent increments after each fixed ii give

P(ξ∗(n)≥r)≤(n+1)qr−1.(3)\mathbb P(\xi^*(n)\ge r)\le(n+1)q^{r-1}. \tag{3}

In particular, for each fixed λ>α\lambda>\alpha and large nn,

P(ξ∗(n)>λlog⁡n)≤Cd,λn1−λ/α.(4)\mathbb P(\xi^*(n)>\lambda\log n) \le C_{d,\lambda}n^{1-\lambda/\alpha}. \tag{4}

Lower tail. Fix 0<λ<α0<\lambda<\alpha. Set h=⌈log⁡n⌉h=\lceil\log n\rceil, r=⌈λlog⁡n⌉r=\lceil\lambda\log n\rceil and b=rhb=rh. Write qhq_h for the probability of a first return to zero within hh steps. The heat-kernel consequence above gives q−Cdh1−d/2≤qh≤qq-C_dh^{1-d/2}\le q_h\le q, hence qh→q>0q_h\to q>0.

Within a block of bb steps, the event that each of the first rr successive return times to the block's starting site is at most hh has probability pn=qhrp_n=q_h^r. Strong Markov gives the product; on this event all rr returns fit in the block and produce r+1>λlog⁡nr+1>\lambda\log n visits. Moreover,

log⁡pnlog⁡n=rlog⁡nlog⁡qh⟶λlog⁡q=−λ/α.\frac{\log p_n}{\log n} =\frac r{\log n}\log q_h\longrightarrow \lambda\log q=-\lambda/\alpha.

There are v=⌊n/b⌋v=\lfloor n/b\rfloor disjoint blocks. The success event for each is determined by its own increments, so these events are independent even if their spatial ranges intersect. A success implies ξ∗(n)>λlog⁡n\xi^*(n)>\lambda\log n. Therefore

P(ξ∗(n)≤λlog⁡n)≤(1−pn)v≤exp⁡(−vpn).(5)\mathbb P(\xi^*(n)\le\lambda\log n) \le(1-p_n)^v\le\exp(-vp_n). \tag{5}

Since b=O((log⁡n)2)b=O((\log n)^2) and vpn=n1−λ/α+o(1)vp_n=n^{1-\lambda/\alpha+o(1)}, for every fixed 0<κ<1−λ/α0<\kappa<1-\lambda/\alpha the last bound is at most exp⁡(−nκ)\exp(-n^\kappa) for all sufficiently large nn. This is an inequality: success in a selected block is a sufficient way to obtain a thick site, not a necessary one.

All late times. Apply (5) with λ=(1−ϵ/2)α\lambda=(1-\epsilon/2)\alpha and κ=ϵ/4\kappa=\epsilon/4. The sum of exp⁡(−nϵ/4)\exp(-n^{\epsilon/4}) over integers n≥Nn\ge N is at most CN−ϵ/4C N^{-\epsilon/4} for large NN; for example, its terms are eventually at most n−2−ϵ/4n^{-2-\epsilon/4}.

For the upper side, if 2k≤n<2k+12^k\le n<2^{k+1} violates the upper bound in DnϵD_n^\epsilon, monotonicity gives

ξ∗(2k+1)>(1+ϵ/2)αlog⁡(2k)≥(1+ϵ/4)αlog⁡(2k+1)\xi^*(2^{k+1})>(1+\epsilon/2)\alpha\log(2^k) \ge(1+\epsilon/4)\alpha\log(2^{k+1})

for all sufficiently large kk. By (4) the probability is at most C2−kϵ/4C2^{-k\epsilon/4}. Summing over k≥⌊log⁡2N⌋k\ge\lfloor\log_2N\rfloor proves the upper half of (2), and hence (2) itself. Here log⁡2N\log_2N in the floor means logarithm to base two; all other logarithms are natural.

Letting N→∞N\to\infty in (2) shows that DnϵD_n^\epsilon holds eventually almost surely. Taking a countable sequence of positive ϵ\epsilon tending to zero proves (1). Counting only times 1,…,n1,\ldots,n, as in the original paper, changes the maximum by at most one. □\square

Used in. Hao–Li–Okada–Zheng, Lemma 3.2 and its transient favorite-site theorem. This logarithmic law concerns d≥3d\ge3; the separate planar upper bound has a squared logarithm.

Bears on. Problem 1165 only by comparison: the theorem feeds Hao, Li, Okada and Zheng's Theorem 1.2 on favorite sites in dimension d≥3d\ge3, which the problem page cites as a contrast to the planar answer. It is not an input to that answer.