The method of cumulants for the normal approximation

PROBABILITY SURVEYS(2022)

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摘要
The survey is dedicated to a celebrated series of quantitave results, developed by the Lithuanian school of probability, on the normal approximation for a real-valued random variable. The key ingredient is a bound on cumulants of the type vertical bar kappa(j)(X)vertical bar <= j!(1+gamma)/Delta(j-2), which is weaker than Cramer's condition of finite exponential moments. We give a self-contained proof of some of the "main lemmas" in a book by Saulis and StatuleviCius (1989), and an accessible introduction to the Cramer-Petrov series. In addition, we explain relations with heavy-tailed Weibull variables, moderate deviations, and mod-phi convergence. We discuss some methods for bounding cumulants such as summability of mixed cumulants and dependency graphs, and briefly review a few recent applications of the method of cumulants for the normal approximation.
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关键词
Cumulants, central limit theorems, Berry-Esseen theorems, large and moderate deviations, heavy-tailed variables
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