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July 25, 2024Proceedings of the American Mathematical Society1 citationsOpen Access

Multivariate asymptotic normality determined by high moments

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PHPaweł HitczenkoNWNick Wormald

Key Points

  • Asymptotic normality is determined by high moments of random variables in multiple dimensions, not just one.
  • A new approach reveals that moment convergence is not necessary to establish asymptotic normality.
  • This analysis encompasses joint distributions related to allocation schemes of finite capacity bins and balls, illustrating practical applications of the findings.  A unique insight into high moments broadens understanding of statistical distribution behaviors, potentially influencing further theoretical developments.

Abstract

We extend a general result showing that the asymptotic behavior of high moments, factorial or standard, of random variables, determines asymptotically normality, from the one dimensional to the multidimensional setting. This approach differs from the usual moment method which requires that the moments of each fixed order converge. We illustrate our results by considering a joint distribution of the numbers of bins (having the same, finite, capacity) containing a prescribed number of balls in a classical allocation scheme.

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Cite This Study

Hitczenko et al. (2024) studied this question.

synapsesocial.com/papers/68e5f1abb6db643587585d2ehttps://doi.org/10.1090/proc/17001
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