Gaussian approximation of moments of sums of independent symmetric random variables with logarithmically concave tails
Rafa{\l} Lata{\l}a
TL;DR
This paper investigates the accuracy of Gaussian approximations for the moments of sums of independent symmetric random variables with logarithmically concave tails, providing insights into their probabilistic behavior.
Contribution
It introduces a new analysis of moment approximation for sums of such variables, extending understanding of their distributional properties.
Findings
Gaussian moments closely approximate sums' moments under certain conditions
Logarithmically concave tails enable effective moment approximation
Results improve existing bounds on moment deviations
Abstract
We study how well moments of sums of independent symmetric random variables with logarithmically concave tails may be approximated by moments of Gaussian random variables.
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