Berry–Esseen dependency-graph conjecture

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Let (Yk)kotinV(Y_k)_{k otin V} be a family of NN random variables admitting a dependency graph of degree D∈Z≥0D\in\mathbb{Z}_{\geq 0}. Assume that there \exists δ∈(2,3]\delta\in(2,3] such that Aδ<∞\mathcal{A}_\delta<\infty, where Aδ=∑k∈VE[∣Yk−ck∣δ]\mathcal{A}_\delta=\sum_{k\in V}\mathbb{E}[|Y_k-c_k|^\delta], and let WW be the standardized \sum and ξδ\xi_\delta the renormalized standard deviation defined by

ξδ=(NAδ)1/δVar⁡(S)N(D+1).\xi_\delta=\left(\frac{N}{\mathcal{A}_\delta}\right)^{1/\delta}\sqrt{\frac{\operatorname{Var}(S)}{N(D+1)}}.

Berry–Esseen dependency-graph conjecture. The bound

dKol(W,N(0,1))≲ξδ−δ(D+1N)(δ−2)/2d_{\mathrm{Kol}}(W,\mathcal{N}(0,1))\lesssim \xi_\delta^{-\delta}\left(\frac{D+1}{N}\right)^{(\delta-2)/2}

should hold.

This would extend the finite-moment Berry–Esseen estimate from independent variables to variables with a sparse dependency graph, with the effective sample-size factor N/(D+1)N/(D+1). The paper presents this as a conjecture; no resolution is supplied in the given text.

References

Primary source

Maximilian Janisch and Thomas Lehéricy, “Berry-Esseen-type estimates for random variables with a sparse dependency graph”, arXiv:2212.02590 (2023).

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