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Entropic CLT for Order Statistics

Information Theory 2022-05-11 v1 math.IT Statistics Theory Machine Learning Statistics Theory

Abstract

It is well known that central order statistics exhibit a central limit behavior and converge to a Gaussian distribution as the sample size grows. This paper strengthens this known result by establishing an entropic version of the CLT that ensures a stronger mode of convergence using the relative entropy. In particular, an order O(1/n)O(1/\sqrt{n}) rate of convergence is established under mild conditions on the parent distribution of the sample generating the order statistics. To prove this result, ancillary results on order statistics are derived, which might be of independent interest.

Keywords

Cite

@article{arxiv.2205.04621,
  title  = {Entropic CLT for Order Statistics},
  author = {Martina Cardone and Alex Dytso and Cynthia Rush},
  journal= {arXiv preprint arXiv:2205.04621},
  year   = {2022}
}

Comments

Accepted to the 2022 IEEE International Symposium on Information Theory (ISIT)

R2 v1 2026-06-24T11:12:19.464Z