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Let $X_1,\dots, X_n$ be independent non-negative random variables (with finite expectation and variance), and $0 < m < n$ be a fixed integer such that there exists a subset $S\subseteq [n]$ of size $\lvert S\rvert =m$ with

  1. $\sum_{k\in S} \mathbb{E}[X_k] \geq 100$; and
  2. $\sum_{k\notin S} \mathbb{E}[X_k] \leq 1$

and

  1. $\sum_{k\in S} \operatorname{Var}[X_k] \leq \frac{1}{100} \left(\sum_{k\in S} \mathbb{E}[X_k]\right)^2$; and
  2. $\sum_{k\notin S} \operatorname{Var}[X_k] \leq \frac{1}{100}$.

In other words, there is an unknown subset of elements that, altogether, are "heavy", and its complement is "light." If I define $T$ as the indices of the $2m$ top values among $X_1,\dots, X_n$, what is the probability that $\sum_{[n]\setminus T} \mathbb{E}[X_i] < 2$? (where the probability is taken over the realization of the $X_i$'s). In other terms, what is the probability that we removed (almost) all the weight of the"heavy set"?

If $m=1$, then this is clear that is happens with high probability by Chebyshev's inequality: if $S=\{i\}$, then the probability that $X_i$ is less than $50$ is at most $\frac{1}{25}$, and similarly the probability that the sum of all other elements (so a fortiori any of them) is greater than 2 is at most $\frac{1}{100}$, so that overall $X_i$ is the greatest element (and therefore among the two top elements) with probability at least $19/20$. Is there a way to generalize this argument to $m \geq 2$?

PS: as a disclaimer, this question was first asked on Math.SE 10 days ago; I am cross-posting it because, in spite of this delay and a bounty I put on it (and expired this morning), it has not received any answer. (And I'm hoping it may be more suited to this website.) I hope this is OK; however, if this appears unacceptable to the moderators, feel free to close this question...

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    $\begingroup$ Hmm, how about this bad example? $X_1 = 100$ deterministically. $X_2 = 4$ with prob $0.5$ and $0$ with prob $0.5$. All other $X_i$ are, say, $1/n$ deterministically, and choose any $2 \leq m < n/2$. Now I think all the hypotheses are satisfied, yet with probability $1/2$ the smallest realized value is $X_2$, whose expectation is $2$. ... so I guess, if the example is correct, we need every member of $S$ to have small variance... $\endgroup$
    – usul
    Commented Aug 18, 2015 at 1:05
  • $\begingroup$ @usul Good point... I am going to think about the original problem to see if this applies, but I am not sure the small-variance assumption actually holds. (the actual problem I have is to design a way to do what is asked — "sieve out most of the weight of the heavy set," while asking only for a constant number of independent realizations of each individual $X_i$ — and doing the above was basically the simplest and most natural (and only?) way I could think of... $\endgroup$
    – Clement C.
    Commented Aug 18, 2015 at 11:17

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