Convergence of the probability of large deviations in a model of correlated random variables having compact-support $Q$-Gaussians as limiting distributions
Max Jauregui, Constantino Tsallis

TL;DR
This paper studies the convergence properties of correlated binary random variables with scale-invariant distributions, revealing non-classical large deviation behaviors and convergence to beta distributions, challenging traditional law of large numbers assumptions.
Contribution
It demonstrates that for a specific class of correlated variables, the distribution of their normalized sum converges to a beta distribution, and large deviations do not vanish as in independent cases.
Findings
Distribution of $S_n/n$ converges to a beta distribution with parameters $ u$.
Large deviations do not tend to zero, violating the law of large numbers.
Probability of zero sum decays as a power law $1/n^ u$.
Abstract
We consider correlated random variables taking values in such that, for any permutation of , the random vectors and have the same distribution. This distribution, which was introduced by Rodr\'iguez et al (2008) and then generalized by Hanel et al (2009), is scale-invariant and depends on a real parameter ( implies independence). Putting , the distribution of approaches a -Gaussian distribution with compact support () as increases, after appropriate scaling. In the present article, we show that the distribution of converges, as , to a beta distribution with both parameters equal to . In particular, the law of large numbers does not hold since, if , then ,…
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