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I am looking for good and explicit lower and upper bounds on the probability that $x^TGx\ge 0$, where $G$ is a symmetric matrix with zero trace and $x$ is a vector whose components are independent random variables uniformly distributed in either $[-1,1]$ or $\{-1,1\}$.

(What I really need are realistically general explicit sufficient conditions on $G\in R^{n\times n}$ for the probability to be $\ge p$ for some $p>0$ independent of the dimension $n$. Thus the bounds should be strong enough to deduce such conditions and to prove that the gap between sufficient and necessary is not too large.)

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  • $\begingroup$ Are moment bounds sufficient? (e.g. en.wikipedia.org/wiki/Second_moment_method) $\endgroup$ Commented Nov 23, 2015 at 16:07
  • $\begingroup$ Only if one can deduce from them good bounds on the probabilities. $\endgroup$ Commented Nov 23, 2015 at 16:12
  • $\begingroup$ Well, the quality of the bounds will depend on what your $G$ is. If you don't have a specific $G$ in mind, then it would depend on what your requirements for "good" are. Try it and let us know if it is sufficient. $\endgroup$ Commented Nov 23, 2015 at 16:17
  • $\begingroup$ My $G$'s are certain parametric matrices of arbitrary dimension. The trace of $G$ is zero; so the second-moment method gives no result. $\endgroup$ Commented Nov 23, 2015 at 16:19

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