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Oct 31, 2019 at 3:56 comment added TOM I cannot do the computation now, but I am reasonably sure it leads to a decent dependency on $p$.
Oct 31, 2019 at 3:53 comment added TOM I see. One last thought in the situation with $p>\frac{1}{2}$. For all $h\in \mathbb{R}$ the moment generating function $\mathbb{E} \exp( a_{1}X_{1}+\cdots+a_{n}X_{n})$ is maximized when $a_{i}=\frac{1}{n}$ (for convex linear combinations, here $\mathbb{E}$ stands for expectation). Now $\mathbb{P}(S\geq \frac{1}{2})=1-\mathbb{P}(S< \frac{1}{2})\geq 1-\mathbb{E} \exp(h(S-\frac{1}{2}))$ for $h<0$ (standard Hoeffding's trick). Now using the fact that the worst case weights are all equal and optimizing with respect $h<0$ yields a bound dependent only on $p$.
Oct 30, 2019 at 9:30 comment added Ron P TOM, please note that we are interested in the probability of the event that S>p rather than S>1/2.
Oct 30, 2019 at 1:01 comment added TOM In case there still is confusion, here is the link to the paper (the result of interest is stated on the very first page): core.ac.uk/download/pdf/82145794.pdf
Oct 29, 2019 at 12:28 comment added TOM I just did. It is the full result with $p\geq 0.5$.
Oct 29, 2019 at 2:26 comment added kodlu since links are not permanent can you please write out your answer explicitly?
Oct 29, 2019 at 2:17 comment added TOM I forgot to add that this is true only for $p\geq \frac{1}{2}$.
Oct 28, 2019 at 14:02 comment added TOM The latter bound is sharp as equality is achieved by taking just one such random variable.
Oct 28, 2019 at 14:02 comment added TOM Ligett proved that for independent Bernoulli random variables $X_1, X_2, \ldots, X_n$ with parameter p. Let S be a convex combination of these variables. Then $$\mathbb{P}(S\geq 1/2) \geq p.$$
Oct 27, 2019 at 11:33 comment added Ron P Can you elaborate, TOM?
Oct 25, 2019 at 19:09 history answered TOM CC BY-SA 4.0