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Oct 4, 2015 at 0:01 comment added Anthony Quas I think roughly the same argument works for other values of $t$. Define $Y_n$ to be 1 if $a_1+\ldots+a_n>t\sqrt n$ and 0 otherwise. When you do the Brownian motion approximation argument with $m<n$, you end up with Cov$(Y_m,Y_n)$ $\approx \mathbb P(N_1>t;N_2>t\sqrt{n/(n−m)}−N_1\sqrt{m/(n−m)}−\mathbb P(N_1>t)^2$ where $N_1$ and $N_2$ are independent standard normals. The weighting means you don't have to worry about terms with $m$ and $n$ within a bounded factor of each other. Outside this range, this is close to 0 as before so that you get the desired convergence.
Oct 3, 2015 at 19:17 comment added James Propp Yes, I believe this would work. So that settles the case $t=0$. But it's less clear to me how to handle other values of $t$ (from the original problem).
Oct 3, 2015 at 19:15 vote accept James Propp
Oct 3, 2015 at 14:50 comment added Anthony Quas So I was thinking about this. I think the point is that the sequence $S_N$ is very slowly varying indeed. Nothing much can happen between times $e^{(1+\epsilon)^k}$ and $e^{(1+\epsilon)^{k+1}}$. So if the sequence goes to 0 with probability 1 along that sequence of times for each $\epsilon$, you get convergence for the full sequence. Now work with a countable sequence of $\epsilon$ going to 0. An argument of this type (for standard weights) appears in notes of Wierdl and Rosenblatt in the Cambridge Volume "Convergence in Ergodic Theory"
Oct 3, 2015 at 13:49 comment added James Propp Modulo Brownian motion calculations that I haven't checked, this looks good, as far as it goes. But how do we get to "converges to 1/2 with probability 1"? Given how slowly 1/log $N$ goes to 0, I don't see why the outliers in the sequence $S_1,S_2,...$ are still constrained to go to 0. There's probably a simple argument for this, but off the top of my head I don't see it.
Oct 3, 2015 at 11:58 history edited Anthony Quas CC BY-SA 3.0
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Oct 3, 2015 at 5:08 history answered Anthony Quas CC BY-SA 3.0