Name
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 $O(1/\sqrt{n})$ 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ISIT50566.2022.9834720 | International Symposium on Information Theory (ISIT) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martina Cardone | 1 | 47 | 18.36 |
| Alex Dytso | 2 | 45 | 20.03 |
| Cynthia Rush | 3 | 0 | 0.34 |