Estimates of moments and tails of Gaussian chaoses
classification
🧮 math.PR
keywords
estimateschaosesgaussianmomentstailsconstantsdependingderive
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We derive two-sided estimates on moments and tails of Gaussian chaoses, that is, random variables of the form $\sum a_{i_1,...,i_d}g_{i_1}... g_{i_d}$, where $g_i$ are i.i.d. ${\mathcal{N}}(0,1)$ r.v.'s. Estimates are exact up to constants depending on $d$ only.
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Cited by 1 Pith paper
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Finite-Order Hilbertian Gaussian Random Tensor Estimates
Establishes that the L^p(Ω; S_r) norm of a finite-order decoupled homogeneous Gaussian chaos operator is bounded by C_m (p+r)^{m/2} times the maximum oriented Schatten flattening norm of its kernel.
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